The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Economic
Review
Federal Reserve Bank
of San Francisco
1993
Number 2
.
Adrian W. Throop
A Generalized Uncovered Interest Parity Model
of Exchange Rates
Elizabeth S. Laderman
Determinants of Bank Versus Nonbank
Competitiveness in Short-term Business Lending
Carl E. Walsh
What Caused the 1990-1991 Recession?
A Generalized Uncovered Interest Parity Model of Exchange Rates ......................... .... 3
Adrian W. Throop
Determinants of Bank Versus Nonbank Competitiveness
in Short-term Business Lending..................................................................................... 17
Elizabeth S. Laderman
What Caused the 1990-1991 Recession? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Carl E. Walsh
Opinions expressed in the Economic Review do not neces
sarily reflect the views of the management of the Federal
Reserve Bank of San Francisco, or of the Board of Governors
of the Federal Reserve System.
The Federal Reserve Bank of San Francisco’s Economic Review is
published three times a year by the Bank’s Research Department under
the supervision of Jack H. Beebe, Senior Vice President and Director of
Research. The publication is edited by Judith Goff. Design, production,
and distribution are handled by the Public Information Department,
with the assistance of Karen Flamme and William Rosenthal.
For free copies of this and other Federal Reserve publicatons, write or
phone the Public Information Department, Federal Reserve Bank of San
Francisco, P.O. Box 7702, San Francisco, California 94120. Phone
(415) 974-2163.
Printed on recycled paper
with soybean inks.
A Generalized Uncovered Interest Parity Model
of Exchange Rates
Adrian W. Throop
Research Officer, Federal Reserve Bank of San Francisco.
The author wishes to acknowledge the able research assistance of Sean Kelly and the helpful comments of the editorial committee, consisting of Ramon Moreno, Michael
Dooley, and Bharat Trehan.
Sticky price monetary models of exchange rates, while
reasonable theoretically, have been disappointing empirically. Out-ofsample predictions have been little or no
better than those from a naive model of no change. The
most likely reason is that shocks to the market's expectation of the future equilibrium real exchange rate weaken
the stability ofthe association between exchange rates and
the real interest rate differentials. This study identifies
three types ofshocks that appear to be empirically important. These are productivity growth, which changes the
relative price of traded goods at home versus abroad,
government budget deficits, and the real price of oil.
Thesefactors along with real interest rates are shown to
explain at least 80 percent of the longer run variation in
both the trade-weighted dollar and bilateral rates against
the dollar. An error correction model that includes these
factors is shown to have out-ofsample prediction errors
for changes in the trade-weighted dollar that are 30 to 45
percent lower than those from a naive model ofno change,
at horizons offour to eight quarters. The prediction errors
for bilateral rates against troe dollar are almost as low.
This paper reexamines the sources of fluctuations in exchange rates between the dollar and other major currencies
in the post 1973 flexible rate experience. The real values of
the major currencies have fluctuated quite Widely in this
period. As a result, flexible-price models, which assume
constant real currency values (or purchasing power parity),
have not been successful in explaining their movements.!
Sticky price monetary models, which assume that prices in
markets for goods adjust to disturbances more slowly than
prices in markets for financial assets, have appeared to
work more satisfactorily. 2 In these models, purchasing
power parity holds in the long run when prices are able to
adjust fully, but deviations from purchasing power parity
occur in the short run. These deviations are associated with
temporary differentials between real interest rates at home
and abroad. Uncovered interest parity holds in the sense
that the differences between real interest rates are offset by
expected changes in the real exchange rate. As a result,
movements in the real exchange rate can be explained by
changes in the differential between home and foreign real
interest rates.
The robustness of the sticky price monetary model has
been challenged in an important series of papers by Meese
and Rogoff, however. 3 They showed that, while variations
in interest differentials can explain some of the movements in the major currencies within the period of estimation of the model, predictions outside that period are no
better than those of a naive model of no change, even when
actual values of the explanatory variables are used. Meese
and Rogoff further suggested that the most likely hypothesis for explaining this result is the existence of shocks to the
flexible-price equilibrium of the real exchange rate. The
resulting variation in the expected value of the future real
exchange rate would weaken the statistical association
between real interest differentials and exchange rates. Yet,
it has been difficult to identify which real factors have
affected equilibrium real exchange rates over what periods.
1. Studies of the U. S. dollar using data for the period 1973 to 1978 have
yielded findings consistent with the flexible price model, but those
incorporating more recent data have been much less favorable to that
approach. See Bilson (1978) and Hodrick (1978).
2. See, for example, Dornbusch (1976), Frankel (1979), Hooper and
Morton (1982), and Shafer and Loopesko (1983).
3. See Meese and Rogoff (1983a, 1983b, 1988) and also Meese (1990).
4
FRBSF ECONOMIC REVIEW
1993,
NUMBER
2
This paper identifies several important factors in addition to real interest rate differentials that have altered real
exchange rates between the major currencies. These factors include productivity in traded and nontraded goods,
the real price of oil, and government budget balances. We
call the resulting extension of the sticky price monetary
model a generalized uncovered interest parity model of the
exchange rate. It is shown that exchange rates are cointegrated with these factors over time. Because of this, real
exchange rates can deviate from a simple purchasing power
parity relationship even in the long run. Moreover, the
adjustment of currencies to the equilibrium values determined by these factors is a major part of their short-run
fluctuation. In particular, out-of-sample predictions (using
actual values of the explanatory variables) of both the
nominal and real trade-weighted dollar, as well as of three
bilateral rates against the dollar, from an error-correction
form of the generalized model are shown to be significantly
better than those from a naive model of no change.
Section I reviews the basic elements of the conventional
sticky price model ofexchange rates and previous tests ofits
out-of-sample predictive power. Section II develops the
generalized uncovered interest parity model. In Section III
it is shown that the exchange rate is cointegrated with productivity differentials, the real price of oil, government
budget balances, and the long-term real interest rate differential. Then, an error correction model is estimated to
capture the gradual adjustment of exchange rates to the
longer-run equilibrium established by this larger combination of variables. Out-of-sample predictions from this generalized uncovered interest parity model are shown to be
very much superior to those of a naive model of no change.
Section IV provides a summary and some conclusions.
I.
CONVENTIONAL UNCOVERED INTEREST
PARITY MODEL
Much of the recent work on exchange rates has been based
upon the "monetary" or "asset" view. The market rate of
exchange between two currencies is seen in the short run to
equilibrate the international demand for stocks of assets,
rather than the international demands for flows of goods, as
the more traditional view posits. However, market adjustment ensures equilibrium in the goods markets as well
in the longer run. The most widely used approach has been
the uncovered interest parity model.
Uncovered Interest Parity
The conventional uncovered interest parity model of exchange rates uses two basic building blocks: (1) uncovered
interest parity and (2) purchasing power parity. The condi-
tion of uncovered interest parity says that market arbitrage
will move the exchange rate to the point at which the
expected rate of return on investments denominated in
either the home or foreign currency is the same, except for a
possible risk premium. Thus,
(1)
et - Et(e t +k) = k(kit - kin
+ pr
p
where
et = log of nominal value of home currency
=
market expectation at time t of exchange
rate at time t + k
home country interest rate on security with
k periods to maturity
foreign country interest rate on security with
k periods to maturity
prt = risk premium
If the interest rate differential times the periods to maturity,
k, exceed~ the expected rate of depreciation of the home
currency (allowing for a..ny risk premium, prt ), then arbitragers would bid the value of the home currency up until
the equality holds, thus equalizing expected returns at
home and abroad.
A problem with using this form of the theory for predicting exchange rates is that it is difficult to model the expected value of the nominal exchange rate k periods ahead.
One way around this problem is. to state the uncovered
interest parity condition in real terms. Given nominal
uncovered interest parity, it is also true that the expected
depreciation in the real value of the home currency equals
the excess in the real rate of return on investments in the
home country over those in the foreign country (times k):4
(2) qt - Et(qt+k) = k( (kit - k'IT t) - (kit ..... k'ITt»)
+ prt
or
(3) qt = E/qt+k)
+ k( (kit
- k'IT t) - (kit - k'ITt»)
+ prp
where
qt = log of real value of home currency
k'ITt = market expectation at time t of inflation rate at
home over k periods
k'ITt*
market expectation at time t of inflation rate
abroad over k periods
4. The uncovered interest parity condition in nominal terms is: e, J'*). But by definition e, = q, + pt - p, and
+ pt+ kk'ITt- p, - kk'IT,. Substituting these relationshipsintothefirstequationthengivesq, - E,(q'+k) = k[(ki, - k'IT,)
- (k i ,* - k'ITnJ·
E,(e'+k) = k (ki, E,(e'+k) = E,(q'+k)
THROOP / GENERALIZED UNCOVERED INTEREST PARITY MODEL
The advantage of equation (3) is that, particularly if a longterm real rate of interest is used, the expected value of
the real exchange rate k periods ahead may be assumed to
be a constant, corresponding to a flexible price equilibrium
of purchasing power parity. Although equation (3) as it
stands predicts the real exchange rate, it can be modified to explain the nominal exchange rate. This is done
by breaking the real exchange rate into its real and nominal
components:
where
pt =
log of overall price level abroad
Pt = log of overall price level at home
The monetary theory of exchange rates explains p* and p
in terms of the· demand for money at home and abroad.
Given a· stable standard demand function for money, the
price level in each country would vary positively with
the money supply and the nominal interest rate and negatively with real income.
Making these substitutions, Meese and Rogoff (1983a)
estimated equation (4) for bilateral values of the dollar
against the mark, pound, and yen. They then made predictions of these exchange rates outside of the period over
which the model coefficients were estimated, using actual
realized values ofall the explanatory variables. The resulting prediction errors were no lower than those from a naive
model that simply assumes no change in the exchange rate.
As a result, it appeared that current structural models ofthe
nominal exchange rate do not describe stable economic
relationships.
However, these results may simply have been due to
instability in the demand for money functions, resulting in
poor predictions of p and p*, rather than to instability
in the basic uncovered interest parity relationship. Therefore, Meese and Rogoff (1988) followed up their earlier
study by making similar out-of-sample predictions from an
estimate of equation (3) making the real exchange rate the
dependent variable. In this version of uncovered interest
parity, current price levels are subsumed inthe definition of
the real exchange rate, which then simply becomes a function of the real interest rate differential and the market's
expectation of the flexible-price equilibrium value of the
real exchange rate. The latter is assumed to be a constant
given by purchasing power parity. Once again, however,
out-of-sample predictions were no more accurate than
those from a naive model of no change.
5
Note that since what is at issue is the stability of the
exchange rate model as indicated by its ability to make
ex post forecasts, rather than ex ante forecasts, there is a
straightforward way of testing the predictive ability of
the nominal version of the model that is independent of the
complications introduced by money demand. This is simply to use equation (4) with the actual realized values of
the price levels, p* and p, on the right hand side to predict
the nominal exchange rate. This equation relies upon the
same basic building blocks of uncovered interest parity and
purchasing power parity as equation (3) for the real exchange rate. Moreover, the prediction errors from the two
equations will be exactly the same because p* and p are
known. 5 Obviously, however, the prediction errors for the
naive model of no change would differ for real and nominal
exchange rates.
Meese and Rogoff (1988) suggest that the most likely
hypothesis for explaining the poor out-of-sample predictions of the conventional model of exchange rates, whether
nominal or real, is the existence of shocks to the flexible
price equilibrium of the real exchange rate. The resulting
variation in the expected value of the future real exchange
rate would weaken the statistical association between the
real interest rate differential and either the nominal or real
exchange rate. To assess the empirical importance of these
effects, this paper expands the conventional model to
include factors that alter the flexible-price equilibrium of
the real exchange rate. This generalized uncovered interest
parity model is then used to generate out-of-sample predictions of both nominal and real exchange rates.
n. GENERALIZED UNCOVERED
INTEREST PARITY MODEL
The expected value of the flexible-price equilibrium of
the real exchange rate, which serves as an anchor for the
conventional uncovered interest parity model, is likely to
change significantly over time in response to a number of
factors. This section expands the model to include some
of these factors.
Productivity Growth
The real exchange rate relevant for uncovered interest
parity is measured in terms of overall price levels. But,
when measured this way, the flexible-price equilibrium
will tend to change over time as a result of differential rates
5. The error in predicting the log of the real exchange rate is: qt - qt or
et - pt+ p/ - (et~ N+ fiJ But since the price levels are known,N
= pt and fit = p.Therefore, qt- qt = et - et'
6
FRBSF ECONOMIC REvIEW 1993, NUMBER 2
of productivity growth between traded and nontraded
goods. This can be seen by examining the relationship
between the real exchange rate when measured in terms of
overall price levels and when measured in terms of the
prices of tradable goods.
The log of the real value of the home currency in terms of
overall price levels is:
In empirical analysis, the wholesale price index is
frequently used as a proxy for the prices of traded goods. 7
That approach is also followed here. Returning to equation
(3) for uncovered interest parity with the real exchange
rate, equation (7) can be substituted for the expected value
of the real exchange rate,giving:
(8)
qt = B o
+ Et(qdt + k )
(5)
where
et
=
log of nominal value of home currency
Pt
=
log of overall price level at home
where
kRt - kR1 = (kit - k7T t) - (k i1- k7T f)·
p1 = log of overall price level abroad
Next, the log of the real value of the home currency in
terms of the prices of traded goods is:
(6)
Next, the expected difference between relative prices at
home and abroad is assumed to be a linear function of the
current difference, so that:
(9)
where
pdt = log of price of traded goods at home
pd1 = log of price of traded goods abroad
Substituting (6) into (5), the relationship between the real
exchange rates measured in these two different ways is
therefore:
(7)
qt = qdt
+ (pd1- p1) - (pdt - Pt)·
Thus, even if the real exchange rate in terms of the prices of
traded goods remains constant according to purchasing
power parity, the real exchange rate in terms of overall
price levels varies according to whether the relative price of
traded goods is changing by more or less than abroad.
Since productivity typically grows faster in the traded
goods sector than in the non-traded goods sector, the
relative price of traded goods typically falls over time.
Should the relative price of traded goods fall faster at home
than abroad, then the real value of the home currency in
terms of overall prices would rise even though the real
exchange rate in terms of traded goods prices remains
constant. The theory of purchasing power parity suggests
that the exchange rate should adjust to equalize the prices
of traded goods at home and abroad in terms of the same
currency. But even if purchasing power parity holds in this
sense, the flexible price equilibrium of the real exchange
rate in the uncovered interest parity model would vary over
time according to differential productivity growth. 6
6 .. This aspect of the purchasing power parity hypothesis was explored
in a well-known article by Belassa (1964). A more recent treatment is
Koedijk and Schotman (1990).
+ B 3 (kRt - kR1)·
If purchasing power parity holds in traded goods, then
E(qdt+k) is simply a constant. But home and foreigntraded goods by and large are imperfect substitutes, so that
purchasing power parity does not hold even for traded
goods. International adjustment requires changes in the
prices of home-traded goods relative to foreign-traded
goods, and therefore in the real exchange rate measured in
terms of the prices of traded goods.
Budget Balances
An important factor requiring such adjustment is changes
in the balance between saving and investment at home
relative to that abroad. A country with a high rate of
investment relative to saving will tend to absorb more
output than it produces, which will tend to put upward
pressure on the prices of home-traded goods relative to
those of foreign-traded goods. Historically, private saving
has· been quite stable. 8 In the last two decades, however,
7. See, for example, Koedijk and Schotman (1990), Clements and
Frenkel (1980), and Wolff (1987).
8. See David and Scadding (1974). As pointed out by Feldstein (1992),
private saving in the U.S. has trended down in the 1980s. However,
foreign private saving rates also have declined in this period. See, for
example, Bosworth (1993, ch. 3). So effects on the dollar have tended to
be offsetting.
THROOP / GENERALIZED UNCOVERED INTEREST PARITY MODEL
u. S. government saving has fluctuated a lot. Consequently,
this paper focuses on the effects of changes in government
saving.
The effects .of changes in government saving on the
flexible-price equilibrium of the real exchange rate can be
illustrated with the aid of a simple model. 9 This assumes
that traded goods produced in different countries are
imperfect substitutes, so that the equilibrium price of
traded goods in one country relative to that in another
changes in response to shifts in supply and demand. In
contrast and consistent with the notion of uncovered interest parity, financial assets as a first approximation are
assumed to be perfect substitutes (this assumption will be
relaxed later). Real aggregate spending at home and abroad
varies inversely with the country's real interest rate; and
the real trade balance moves inversely with the real value
of the country's currency measured in terms of prices of
tradable goods. In algebraic terms, the conditions for full
employment at home and abroad are therefore:
(10)
Yo = A(R)
y~ =
(11)
A*(R*)
+
nx(qd)
+ nx*(qd)
where
YoY~ =
A(A *)
full employment output
=. real aggregate spending, or absorption
nx(nx*) = real net exports
(R,R*) =. real interest rate
qd = real value of home currency measured in prices
of tradables
Assuming a complete adjustment to full employment equilibrium, there are three unknowns (R, R*, and qd) but only
two equations. However, R can be solved as equal to R* in
the case where home and foreign assets are perfect substitutes (or equal to R* plus or minus a risk premium in the
case of imperfect substitutes).
Figure I provides a graphical representation of this
system. The goods market equilibrium in the home country
is represented by Gh • It is downward sloping because a
reduction in the domestic real interest rate (left scale) must
be offset by an appreciation in the real value of the home
currency (qd) in order to maintain real aggregate spending
7
FIGURE I
EFFECT OF FISCAL EXPANSION IN HOME COUNTRY:
SHORTER RUN
,
, ,
~
Real Value of Home Currency, qd
~
Depreciation
Appreciation ~
to be offset by a depreciation in the real value of the home
currency in order to restore a goods market equilibrium
abroad. General equilibrium in the case of perfect substitutability between assets lies at point a, where the two
schedules for goods market equilibrium intersect and the
interest rates are equalized.
Consider now the comparative statics of a fiscal expansion in the home country. A fiscal expansion in the form of a
larger budget deficit or smaller surplus at home increases
the demand for home goods, shifting the Gh schedule up
and to the right. 10 Either a higher real interest rate or higher
real value ofthe domestic currency, or some combination of
the two, is necessary to maintain the same level of aggregate spending on home goods as before.
To the extent that higher domestic spending falls on
foreign goods, the Gf schedule shifts up also. But one
would expect more of the increased spending to fallon
home goods than foreign goods. So the Gh schedule would
shift up by more than the Gf schedule, leading to a new
general equilibrium at a point like b. At this point the world
level of real interest rates will have risen, and the real value
of the home currency (qd) will have appreciated in response to the fiscal expansion at home.
equal to potential output. Conversely, the locus of the
foreign goods market equilibrium, Gf' is upward sloping.
A reduction in the foreign real interest rate (right scale) has
9. For earlier treatments, see Dornbusch (1983), Blanchard and Dornbusch (1984), Hutchison and Throop (1985) and Throop (1989).
10. This assumes that households do not increase their saving in order to
fund the extra future tax liabilities caused by the increase in the
government's larger deficit. If they did increase their saving by the full
amount ofthe increase in these liabilities, then the Gh and Gfschedules
would not change at all. On this so-called "Ricardo effect," see Barro
(1974), Bernheim (1987), Brunner (1986), and Tobin (1980, ch. 3).
8
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
Even if the fiscal deficit were to persist, however, the
value of the home currency could begin to depreciate and
eventually end up lower than it was before. This would
happen if there were a limit to the amount of home
currency assets that foreigners were willing to absorb.
Associated with the net import surplus resulting from the
home currency's appreciation is a net capital inflow into
the home country. As a result, as foreigners increase their
holding of home country assets, the risk premium on them
is likely to increase, driving a growing wedge between
home and foreign country interest rates.
This process is illustrated in Figure 2. Assume for
simplicity that there is no risk premium to begin with.
Then the fiscal expansion shifts the G k and Gfschedules up
as before. This makes the dollar appreciate from point a to
point b, as before. But now the risk premium grows with
the accumulation of home country debt by foreigners. The
risk premium, given by cd in the diagram, drives a growing
wedge between foreign and home country interest rates.
The risk premium will continue to grow until the exchange
rate has depreciated by enough to prevent net indebtedness
to foreigners from growing any further. If the budget
deficit persists, this would occur when the home currency
has depreciated by enough not only to eliminate the original import surplus but also to generate an export surplus
sufficient to pay for servicing the debt without further
capital inflows. Thus, given a persistent fiscal deficit, a
stable equilibrium requires that the home currency depreciate by more than the original appreciation.
The movements in the real exchange rate that are pro-
FIGURE 2
EFFECT OF FISCAL EXPANSION IN HOME COUNTRY:
LoNGER RUN
R
~
Real Value of Home Currency, qd
~
Depreciation
Appreciation
~
duced by changes in budgetary positions in this comparative statics exercise correspond to changes in the long-run
flexible-price equilibrium of the real exchange rate in the
uncovered interest parity model. The actual effect of
budgetary changes on the exchange rate in the short run
will depend upon the character of market expectations. In
particular, what matters is whether the market views
changes in budgetary positions as temporary or permanent,
the effective time horizon over which its expectations are
formed, and the degree to which the risk premium can be
expected to change as indebtedness changes. Although the
very long-run effect of a persistent fiscal expansion would
appear to be one of depreciation in the real value of the
home currency, the market may well expect an appreciation
to result over its relevant time horizon.
Real Price of Oil
The final factor that appears to have been important in
affecting the flexible-price equilibrium value of real exchange rates is the real price of oil. The real price of oil rose
65 percent in the early 1970s, and then another 70 percent
in the late 1970s and early 1980s as the result of the actions
of the OPEC cartel. Then in the mid-1980s it dropped by
50 percent as the cartel's power started to erode.
Like the effects of budget deficits, the effects of oil price
changes on the flexible-price equilibrium value of real
exchange rates between currencies of the oil-importing
countries depend upon the effects on the goods markets of
those countries. Following oil price increases, the less
developed oil-exporting countries typically have temporarily invested the proceeds of higher oil export revenues in
the capital markets of the developed oil-importing countries, which in tum have lent much of these funds to other
less developed countries. In this "recycling" process international capital mobility has been fairly high, so that it can
be assumed real interest rates in different countries would
continue to be roughly equalized in flexible-price equilibrium. As a result and similar to the effects of budget
deficits, the effect of an oil price change on equilibrium
exchange rates of the oil-importing countries depends upon
the relative effects on aggregate demand in those countries. These effects may change over time to some degree,
as the oil-exporting countries gradually increased their
expenditures on the exports of oil-importing countries.
However, the most important factor is the degree of dependence of the importing countries on imported oil. This
can be illustrated with the aid of the model used in the
previous section.
Industrialized countries differ widely in their dependence on imported oil. For instance, the U.S. imports about
40 percent of its oil, but Japan is totally dependent on
THROOP / GENERALIZED UNCOVERED INTEREST PARITY MODEL
imports to satisfy its oil needs. Let the home country in the
model be like the U.S., which is less dependent upon oil
imports than its major industrialized trading partners.
The other country in the model represents those trading
partners. 11
Following a price increase by OPEC, in the first stage
assume that all of OPEC's oil revenues are invested abroad.
If the home country is less dependent upon imported oil
than its industrialized trading partners, its import bill will
increase but by less than theirs. The increase in the import
bill reduces aggregate demand, and so requires a reduction
in the real interest rate to maintain full employment
equilibrium. As shown in Figure 3, the Gh schedule for the
home country therefore shifts down, but by less than the Gf
schedule for the other oil importers. The result is a decrease in the world real interest rate because of the increase
in OPEC's saving and a real appreciation of the home
currency (in terms of tradable goods prices). The currency
of the foreign country, which is more dependent on imported oil than the home country, depreciates so as to allow
it to export more to the home country in order to pay for its
oil imports more cheaply.
Over a longer run, OPEC will gradually increase the
proportion of oil revenues that are spent on foreign goods
and services. This increases the demand for exports of both
the home country and the foreign country in the model. But
so long as OPEC does not have a much stronger preference
11. The exchange values of the currencies of the oil exporting countries
do not enter into this analysis because oil is priced in dollars, and these
"petrodollars" are either invested or spent on goods and services abroad.
FIGURE 3
EFFECT OF OIL PRICE INCREASE
for the goods of the foreign country compared with those of
the home country, the real appreciation of the home country's currency will not be undone. Thus, following an oil
price increase' it is likely that the market will expect an
appreciation in the flexible-price value of the real equilibrium exchange rates of those countries that are less
dependent on imported Oil. 12 Notice also that over the
longer run the upward shifts of Gh and Gf will tend to
restore the world rate of interest to its prevIous level.
Generalized Uncovered Interest Parity
The log of the flexible-price equilibrium value of the real
exchange rate, measured in terms of the prices of traded
goods, thus can be assumed to be a function of budget
balances both at home (USBB) and abroad (FBB) and the
log of the real price of oil (LPOIL). This gives:
Elqdt + k ) = 'Yo + 'Yl USBB 1
(12)
+
(13) qt
=
Bo
'Y3 LPOILt •
+ B 5 (k Rt
-
kRn·
The presence of Pt * and P on both the left hand side (in qt)
and right hand side of (13) produces an automatic correlation between the left and right side variables. To avoid this
statistical problem when estimating the coefficients of the
model, the dependent variable is redefined to be the nominal value of the home currency by substituting (5) for qt.
Collecting terms, this gives the generalized uncovered
interest parity condition for the nominal exchange rate as:
+ B1 USBB t + B2 FBB t + B 3 LPOIL
-
kRn·
The estimate of this equation is then used to make out-ofsample predictions of the nominal exchange rate, assuming
the values of all explanatory vllJ.-1ables are known. To make
out-of-sample predictions of the real exchange, known
values of Pt and P; are simply added and subtracted,
~
Real Value of Home Currency, qd
Depreciation
+
+ B 1 USBB t + B1 FBB t + B3 LPOIL
+ B 5 (k Rt
~
'Yl FBB t
Next, substituting (12) into (9) gives the generalized open
interest parity condition for the real exchange rate as:
(14) e = Bo
R
9
Appreciation
~
12. Trehan (1986) detects a positive influence of the price of oil on the
trade-weighted value of the u.s. dollar. Also see Krugman (1983) and
Dunn (1979).
10
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
respectively, to the value of e predicted by the estimated
form of equation (14). But as pointed out earlier, since Pt
and pt are known, the prediction errors for real and
nominal exchange rates are the same.
For the case of the trade-weighted dollar, et is measured
by the multilateral trade-weighted value against 10 major
industrial countries constructed by the staff of the Board of
Governors of the Federal ReserVe System. The interest
rates, ,fit and ,fit, are yields on 1O-year government bonds
less a centered 12-quarter moving average of inflation in
consumer prices. The real price of oil is calculated as the
ratio of the seasonally adjusted producers' price of crude
petroleum to the seasonally adjusted producers' price of
finished goods.
To measure anticipated budget balances, a moving
average of inflation-adjusted high employment budget
balances as a percent of GDP for the most recent four
quarters was used. 13 The alternative of budget balances
over four quarters ahead did not perform as well. Neither did
flexible distributed lags on current and past budget balances. For the trade-weighted dollar, trade weights clearly
should be used in aggregating the rest of the world's relative
prices and real interest rates. But the effect of a foreign
structural budget deficit depends upon the relative size of
the foreign country as well, and the weights should reflect
this. The smaller the foreign country, the larger will trade
generally be as a proportion ofGDP, the steeper will be its G
schedule, and the less the G schedule of the home country
will be changed by a movement of the foreign country's
budget deficit. As a result, the smaller will be the size of the
effect of its own budgetary changes on its exchange rate
with the home country. Therefore, in the case of the tradeweighted dollar, foreign budget balances were weighted by
GDP weights times trade weights.
Theoretically, both foreign and US. budget balances
should be included in the model. However, while the US.
budget balance had the expected estimated effects in all
cases, the estimated effects of foreign budget balances
were close to zero and sometimes of unanticipated sign.
In the case of the trade-weighted dollar, the explanation
appears to be that during the sample period there was
relatively little variation in the weighted foreign budget
balance, as shown in Figure 4. For the bilateral rates the
explanation appears to be different. As discussed, the size
of the effect of a foreign budget deficit on the respective
dollar bilateral rate depends upon the relative size of
13. These budget balances are for state and local, as well as the federal,
government. The recent data was constructed from various issues of the
OEeD Economic Outlook. Back data are from Price and Muller (1984).
FIGURE 4
u.s. AND FOREIGN INFLATION-ADJUSTED
STRUCTURAL BUDGET BALANCES
Percent
4
3
u.s. Budget
Balance
2
* *,
,
,
#.,
' .... '
~
•
•
1
.#
'II'
74
76
78
. :-
~'
a
~
-1
Foreign
Budget Balance
80
82
84
-2
86
88
90
the foreign country. In the sample period, the largest of the
three foreign economies (Japan) was only one-third of
the size of the US. economy. So the effect of its budget
deficit on the dollar bilateral rate would be much smaller
than the effect of the US. budget deficit. The crudeness of
the budget data and sharper perceptions ofU. S. as opposed
to foreign budget deficits also may have been contributing
factors. In any case, because of a lack of significant effects,
foreign budget balances were dropped from the model.
A further point with respect to equation (14) is that the
coefficients on the wholesale price differential (B 4) and
the consumer price differential (1- B4 ) should sum to 1.0.
Unrestricted estimates of these coefficients came close to
meeting this condition, and this constraint was imposed
both in the cointegrating vectors and subsequent error
correction models of the exchange rate.
ill.
STABILITY OF THE GENERALIZED
UNCOVERED INTEREST PARITY MODEL
The generalized open interest parity condition of equation
(14) does not hold instantaneously. This is because perceptions of the flexible-price equilibrium of the real exchange
rate evolve gradually in response to changes in the current
values of their determinants. However, the variables in
equation (14) are cointegrated in the long run and can be
described by an error correction system in the short run.
THROOP / GENERALIZED UNCOVERED INTEREST PARITY MODEL
11
Cointegration
where ECt = qt - Bo - B 1 (,fit - ,fin·
Having found that short-term movements in real long-term
interest rate differentials are no better than a naive model
ofno change for making out-of-sample predictions of real
bilateral exchange rates, Meese and Rogoff (1988) went on
to examine the possibility that real exchange rates adjust
slowly to real interest rate differentials. They rejected this
possibility, however, because they found that real bilateral
long-term interest rate differentials were not cointegrated
with real bilateral exchange rates. Cointegration of these
variables would mean that there is a long-run relationship
between them. 14
Meese and Rogoff used the Engle-Granger two-step procedure to test for cointegration. In the first step one variable is regressed against other variables that are potentially
cointegrated with it. The residuals from the regression are
then tested for stationarity by means of the Dickey-Fuller
test. If nonstationarity of the residuals is rejected, then
the combination of variables can be regarded as cointegrated. 15 Table I substantiates a lack of cointegration between the real exchange rate and the real long-term interest
rate differential for the four exchange rates in this study
using the Dickey-Fuller test. 16
A more powerful test for cointegration is available,
however. As proved by Engle and Granger (1987), any
variables that are cointegrated have an error correction
representation. This means, for example, that if the real
exchange rate· is cointegrated with the real interest rate
differential, then the errors in this relationship are part of a
larger error correction system. Such a two variable system
would be written as:
In this error correction model, the short-run and longrun responses of the variables are allowed to differ, and
all variables are treated as endogenous. In contrast, the
Dickey-Fuller test assumes that short- and long-run responses are the same. It also ignores possible endogeneity
ofthe explanatory variables. As a result, the Dickey-Fuller
test is inefficient. A more powerful test for cointegration is
obtained by maximum likelihood estimation of the complete error correction system, as developed by Johansen
(1988) and Johansen and Juselius (1990).17 Table I shows
that, on the basis of this more powerful test, cointegration
between the. real exchange rate and the real long-term
interest rate differential is accepted for the trade-weighted
dollar and bilateral rates against the mark and pound, but is
rejected for the bilateral rate against the yen. 18
The Johansen procedure also was used to test for cointegration of all variables in (14).19 Since the power of this
(16) ~ (,fit - ,fin = PZEC_ 1
14. A necessary condition for cointegration is that the variables be
nonstationary and also integrated of the same order. See Charemza and
Deadman (1992), ch. 5. Meese and Rogoff (1988) rejected stationarity
for levels of the three real bilateral exchange rates as well as the
corresponding real long-term interest differentials, but not for first
differences. Stationarity can also be rejected for levels of the nominal
and real trade-weighted dollar and the corresponding long-term real
interest rate differential.
'
15. See Engle and Granger (1987) and Engle and Yoo (1987), and
Charemza and Deadman (1992), Ch. 5.
16. Couglin and Koedijk (1990) and Edison and Pauls (1991) report
similar results with respect to the cointegration of exchange rates and
real interest rates using the Dickey-Fuller test.
TABLE 1
TESTS FOR COINTEGRATION OF REAL EXCHANGE
RATE AND REAL LoNG-TERM INTEREST RATE
DIFFERENTIAL,
1974.Ql
TO
1991.Q3
AUGMENTED
DICKEy-FULLER
TEST
Trade-weighted US$
Yen/US$
MarklUS$
PoundlUS$
-2.41
-2.39
-1.68
-1.78
JOHANSEN PROCEDURE
Maximum
Eigenvalue
Trace
16.3 **
4.0
12.94*
15.5 **
19.4**
7.2
17.4*
18.7**
**Significant at 5 percent.
*Significant at 10 percent.
17. In the case of two variables there can be only one unique cointegrating vector. In the more general case of a model with n variables, however, there can be up to n-l unique cointegrating vectors. See Johansen
and Juselius (1990) or Charemza and Deadman (1992, Ch. 6.4).
18. Two lags on the differenced variables were used. Edison and Melick
(1992) also found cointegration between the real trade-weighted dollar
and the real long-term interest rate spread using the Johansen procedure.
19. A necessary condition for cointegration is that the variables be
integrated ofthe same order. As discussed in footnote 14, exchange rates
and real interest rate differentials were found· to be stationa..-j in first
differences but not in levels, or integrated of order one. This is also true
of the other variables in equation (14), with the exception of the Japanese price levels, which were stationary in levels. However, since the
U.S. price level is nonstationary in levels, all of the relative price
variables were nonstationary also, and thus also integrated of order one.
12
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
test is low for cointegration vectors that are close to being
nonstationary, it is reasonable to follow a tes~ procedure
that allows rejection for probability values higher than the
usual 5 or 1 percent. As shown in Table 2, the Johansen
procedure rejects the null of no cointegrating vectors for
the trade-weighted dollar at the 1 percent level; and at that
same level of significance one cointegrating vector is
accepted. Similarly, for the nominal bilateral rates against
the dollar, the null of no cointegrating vector is rejected at
from a 5 to 20 percent level of significance, and one
cointegrating vector is accepted. Thus, the data for both the
trade-weighted dollar and the three bilateral rates are
consistent with cointegration of the variables in the generalized uncovered interest parity model.
Estimates of the cointegrating vectors for the variables in
TABLE
2
TESTS FOR COINTEGRATION OF THE EXCHANGE
RATE WITH ALL VARIABLES IN GENERALIZED
UNCOVERED INTEREST PARITY MODEL,
1974.Ql-1991.Q3
JOHANSEN PROCEDURE
Number of
Cointegrating
Vectors
Maximum
Eigenvalue
Trace
Trade-Weighted US$
0
1
47.3****
25.6
96.8****
49.6
YenlUS$
0
1
35.6***
25.4
78.6****
42.9
MarklUS$
0
1
27.8*
14.1
65.1 *
37.3
Pound/US$
0
1
30.5**
23.5
78.7****
48.2
****Reject at 1 percent
***Reject at 5 percent
**Reject at 10 percent
*Reject at 20 percent
TABLE
equation (14) are given in Table 3, and the resulting
contributions to longer-run changes in the value of the
nominal trade-weighted dollar are shown in Figures 5 and
6. The effect of the real interest rate differential was either
very small or of the wrong sign for the yen and pound
bilateral rates, most likely because· of the difficulty of
measuring long-term inflation expectations. So in these
cases the variable was dropped. Otherwise, the overall effects on the exchange rate are about as anticipated. A 1 percentage point increase in the U. S. budget deficit as a
percent of U.S. GDP is estimated to appreciate the value
of the trade-weighted dollar by 6 percent, with the
value in terms of the pound going up by more and in terms
of the yen and the mark by less. A 10 percent higher real
price of oil is estimated to appreciate the trade-weighted
value of the U.S. dollar by about 3 percent, but less so
against the mark than the other two currencies. Also, the
value of the dollar moves positively with the relative
price of traded goods abroad compared with the U.S., as
anticipated.
The inclusion of factors besides interest rates substantialiy reduces the estimated long-run response of the dollar
to interest rates. Without these additional factors, a 1 percentage point change in the real interest rate differential on
lO-year bonds is estimated to move the trade-weighted
dollar by about 7 percentage points. But with their inclusion the estimated effect drops to about 3Yz percentage
points. Evidently, risk in openinterest arbitrage causes the
response of the dollar to fall well short of the 10 percentage
point response that would tend fully to equalize expected
returns on 10-year bonds.
PREDICTIONS WITH AN
ERROR CORRECTION MODEL
Given cointegration of the variables, the short-run adjustment of the exchange rate to generalized open interest
parity can be captured with an error correction model.
Estimates of such a model for changes in nominal exchange
rates are provided in Table 4. The model explains nearly
3
COINTEGRATION VECTORS FOR NOMINAL EXCHANGE RATES,
1974.Ql-1991.Q3
CONSTANT
USEE,
LPOIL,
(pd,*-pd,)
(p,*-p)
Trade-Weighted US$
4.56
-0.0625
0.281
0.985
0.015
YenlUS$
5.41
-0.0447
0.278
2.03
-1.03
MarklUS$
0.987
-0.0469
0.165
2.89
-1.89
-0.111
0.225
1.55
-0.55
PoundlUS$
-0.218
{11 _11*'
R2
S.E.
0.0341
0.798
0.0678
0.918
0.0813
0.866
0.0690
0.861
0.0666
V"t
"'''t I
0.0179
13
THROOP / GENERALIZED UNCOVERED INTEREST PARITY MODEL
half of the in-sample variation of changes in the tradeweighted dollar (Figure 7) and somewhat lesser proportions of changes in the bilateral rates. A full response of the
trade-weighted dollar to changes in the real interest rate
differential takes only one quarter, consistent with a relatively quick exploitation of arbitrage opportunities. The
FIGURES
CONTRIBUTIONS OF
ALL
ECONOMIC FACTORS
TO VALUE OF TRADE-WEIGHTED DOLLAR
0.8
0.4
Productivity (right scale)
0.6
o. 4
• _," • ",.. ~ -
0.2
u.s. Budget
Deficit
(left scale)
#. •
-'. ....
"
0.0
0.2
., ,.",." " . ,.
" ..
• "
BondRate ".'"
' ••• "
Differential (right scale)
-0.2
,
-0.4
,
-0.2
I
' ' ...
I ' ~\,
'"
,
Oil Price (left scale)
, I
-0.4
74
NOTE:
76
78
00
-.
80
82
84
86
\
88
I
90
\
- -0.6
-0.8
speed of response of the dollar to changes in the other
variables is generally not as fast, suggesting a gradual
formation of longer-term expectations with regard to the
flexible-price equilibrium of the real exchange rate.
Out-of-sample predictions that use data other than those
on which the model was estimated provide an important
test of the stability of the economic relationships in the
model. Therefore, the error correction model was first
estimated for the period 1975.Q2 to 1981.Q4. Then predictions for one, four, and eight quarters ahead were made
using the actual values of the explanatory variables. Predictions ofthe change in the nominal exchange rate were made
with the estimated error correction equation, while the predictions of the change in the real exchange rate were obtained by subtracting off changes in logs of the price levels.
The estimation was then updated to include successively
more quarters, allowing additional out-of-sample predictions to be made. The root-mean~squared error (RMSE)
for (non-overlapping) predictions of the error correction
model was then calculated and compared with that of a
naive model of no change. F tests indicated the lack of
significance of lagged changes in the U. S. budget balance
and the price of oil in most cases, suggesting that only the
error correction part of the model is important for the shortrun response to these variables. Consequently, two sets of
predictions were examined, one including these variables
and the other excluding them.
In logarithms.
FIGURE?
FIGURE 6
QUARTERLY CHANGE IN TRADE-WEIGHTED
TRADE-WEIGHTED DOLLAR AND VALUE PREDICTED
BY
ALL ECONOMIC
FACTORS
DOLLAR AND VALUE PREDICTED
BY
1975 = 100
ALL
ECONOMIC FACTORS
Percent
155
10
135
5
Actual
14
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
As discussed earlier, the RMSEs for predictions of the
real and nominal exchange rates are the same in this exercise, and only the RMSEs for predictions from the naive
model of no change differ. As shown in Table 5, the
RMSEs for out-of-sample predictions of the nominal tradeweighted dollar from the full model are about 10 percent
lower than those of the naive model of no change for one or
two quarters ahead. Then, for four. and eight quarters
ahead the RMSE is about 30 and 45 percent less than for the
naive model, respectively. Also, the partial model that
drops lagged changes in the U. S. budget balance and the
price of oil reduces the RMSE by significantly more at
horizons up to four quarters. Thus, not only does the
generalized uncovered interest parity model fit the insample data for the nominal trade-weighted dollar better
than the simple uncovered interest parity model does, but it
also performs significantly better out of sample as well.
The results for the bilateral rates are almost as good. In
the work of Meese and Rogoff, the RMSEs for the out-ofsample predictions of bilateral rates from the simple uncovered interest parity model were no lower than for those
from the naive model. In contrast, the partial generalized
TABLE 4
ESTIMATED ERROR CORRECTION MODEL OF SHORT-RuN ADJUSTMENT FOR THE NOMINAL EXCHANGE RATE,
1975.Q2-1991.Q3
VARIABLE
YENIUS$
MARKIUS$
POUNDIUS$
-0.0421
( -0.934)
-0.0411
( -1.01)
-0.0156
( -0.333)
-0.0174
( -0.646)
0.0209
(0.486)
-0.0186
( -0.479)
-0.0401
( -0.904)
0.0237
(0.572)
0.0852
(1.32)
0.0306
(0.551)
-0.0165
( -0.239)
-0.0474
(-0.723)
0.0874
(1.58)
0.000806
(0.0115)
-0.297
(0.666)
0.149
(0.335)
0.553
(0.953)
0.354
(1.22)
1.15
(2.56)
-0.0443
(0.460)
0.103
(0.180)
0.220
(0.763)
1.29
(2.91)
0.851
(1.91)
0.447
(0.772)
0.646
(2.23)
-0.150
( -0.333)
1.04
(2.27)
0.897
0.780
(1.57)
(2.71)
TRADE-WEIGHTED
/1USBB t _
M.,POIL t _
1
2
-0.00444
( -0.156)
0.00768
(0.181)
0.0190
(2.58)
0.0269
(3.03)
-0.00158
( -0.210)
-0.00143
( -0.143)
0.227
(1.90)
0.397
(2.57)
0.0228
(0.195)
-0.0901
( -0.561)
(~O.860)
-0.166
( -0.166)
-0.188
(1.94)
-0.234
( -2.87)
S.E.
NOTE: t statistics are in parentheses.
US$
0.0330
0.0552
0.116
(0.923)
-0.109
0.0468
0.182
(1.10)
-0.0119
( -0.069)
-0.268
( -1.76)
0.0558
THROOP/GENERALIZED UNCOVERED INTEREST PARITY MODEL
TABLE
5
OUT-OF-SAMPLE RMSE FOR SHORT-RuN
ADJUSTMENT TO GENERALIZED UNCOVERED
INTEREST PARITY,
1982.Ql
HORIZON
TO
1991.Q3
NAIVE MODEL
Nominal Real
ERROR
CORRECTION
(Nominal and Real)
Full
Partial
Trade-Weighted US$
1
2
4
8
0.048
0.081
0.135
0.235
0.050
0.083
0.138
0.223
0.045
0.072
0.093
0.135
0.041
0.060
0.082
0.133
Yen/US$
1
2
4
8
0.059
0.100
0.154
0.263
0.059
0.097
0.145
0.236
0.066
0.084
0.105
0.190
0.064
0.080
0.097
0.189
MarklUS$
1
2
4
8
0.057
0.094
0.156
0.271
0.055
0.090
0.144
0.244
0.058
0.086
0.117
0.149
0.051
0.069
0.097
0.139
PoundlUS$
1
2
4
8
0.057
0.089
0.159
0.205
0.066
0.111
0.153
0.125
0.066
0.111
0.153
0.125
0.060
0.085
0.120
0.150
model reduces the RMSE for bilateral rates by 5 to 30 percent for a horizon of two quarters and by 25 to 50 percent
for horizons of four or eight quarters.
The RMSEs for the naive model of no change are
approximately the same, whether predictions are made for
nominal or real values of the dollar. Therefore, the marked
superiority of the generalized uncovered interest parity
model over the naive model holds up for the real exchange
rates as well.
IV
SUMMARY AND CONCLUSIONS
Sticky price monetary models of the real exchange rate,
while reasonable theoretically, have been disappointing
empirically. These models imply that real exchange rates
should vary significantly with real interest rate differentials, according to the principle of uncovered interest
parity. But while some statistical association between
exchange rates and interest rates has been found, predictions of real exchange rates using data other than those on
which the model is estimated have not been satisfactory.
The most likely reason is that shocks to the market's
expectation of the future equilibrium real exchange rate
15
weaken the stability of the association between the real
exchange rate and the real interest rate differential.
This study has identified three types of factors that
appear to be empirically important. These are productivity
growth that causes changes in the relative prices of traded
goods at home versus abroad, government budget deficits,
and the real price of oil. These factors along with long-term
real interest rate differentials account for at least 80 percent
ofthe longer-run variation in both the trade-weighted dollar
and bilateral rates against the dollar. However, taking these
additional factors into account reduces the estimated effect
of interest rates on the dollar. The estimated response of
the trade-weighted dollar to a 1 percentage point change
in the differential between lO-year real bond rates drops
from about 7 percent to 3Yz percent in the complete model.
An error correction model, based on this expanded form
of uncovered interest parity explains nearly half of the insample variation in changes in the trade-weighted dollar
and has out-of-sample prediction errors that are 30 to
45 percent lower than those from a naive model of no
change over horizons of four or eight quarters. Moreover,
prediction errors for bilateral rates are almost as low as for
the trade-weighted dollar.
These results have important implications for monetary
policy. Most macroeconometric models stress the role of
real interest rate differentials between the U.S. and abroad
in determining the real value of the dollar. However, this
study has shown that productivity growth, the real price of
oil, and budget deficits also play important roles. Moreover, taking these additional factors into account reduces
the estimated effects of interest rates on the dollar. As a
result, the influence of monetary policy on the international sector of the economy, operating through interest
rates, probably is lower than generally thought.
16
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
REFERENCES
Barro, Robert I 1974. "Are Government Bonds Net Wealth?" Journal
ofPolitical Economy (NovemberlDecember), pp. 1095-1117.
Belassa, B. 1964. "The Purchasing Power Parity Doctrine: A Reappraisal." Journal ofPolitical Economy (December) pp. 584-596.
Bernheim, B. Douglas. 1987. "Ricardian Equivalence: An Evaluation
of Theory and Evidence." In NBER Macroeconomic Annual, ed.
Stanley Fisher, pp. 263-304. MIT Press.
Bi1son~
John. 1978. "The Monetary Approach to the Exchange Rate:
Some Evidence." IMF Staff Papers (March) pp. 48-75.
Blanchard, Olivier, and Rudiger Dornbusch. 1984. "U.S. Deficits, the
Dollar, and Europe." Banca Nazionale del Lavoro (March).
Bosworth, Barry. 1993. Saving and Investment in a Global Economy.
Washington, D.C.: The Brookings Institution.
Brunner, Karl. 1986. "Fiscal Policy in Macro Theory: A Survey and
Evaluation." In The Monetary versus Fiscal Policy Debate, ed.
R.W Hafer, pp. 33-116. Totawa, N.I: Rowman and Allanheld.
Charemza, Wojcieck W, and Derik F. Deadman. 1992. New Directions in Econometric Practice. Aldershot, England: Edward Elgar
Publishing.
Clements, K.W., and IA. Frenkel. 1980. "Exchange Rates, Money,
and Relative Prices." Journal of International Economics, pp.
249-262.
David, Paul A., and John L. Scadding. 1974. "Private Savings: Ultrarationality, Aggregation, and Denison's Law." Journal ofPolitical
Economy (March/April) pp. 225-249.
Dornbusch, Rudiger. 1976. "Expectations and Exchange Rate Dynamics." Journal of Political Economy (December) pp. 11611176.
_ _ _ _ _ . 1983. "Comments" on Jeffrey R. Shafer and Bonnie E.
Loopesko, "Floating Exchange Rates After Ten Years." Brookings Papers on Economic Activity, pp. 79-85.
Dunn, Robert M. 1979. "Exchange Rates, Payments Adjustment,
and OPEC: Why Oil Deficits Persist." Essays in International
Finance, No. 137. Princeton University.
Edison, Hali I, and B. Dianne Pauls. 1991. "Re-assessrnent of the
Relationship Between Real Exchange Rates and Real Interest
Rates: 1974-1990." International Finance Discussion Paper, No.
408. Board of Governors of the Federal Reserve System.
_ _ _ _ , and WilliamR. Melick. 1992. "Purchasing Power Parity
and Uncovered Interest Parity: The United States 1974-1990."
International Finance Discussion Paper, No. 425. Board of Governors of the Federal Reserve System.
Engle, Robert F., and Byung-Sam Yoo. 1987. "Forecasting and Testing
in Cointegrated Systems." In Topics in Applied Regression and
Time Series Analysis, Supplement to Journal of Econometrics,
pp. 143-159.
_ _ _ _ _ , and C.WI Granger. 1987. "Cointegration and Error
Correction: Representation, Estimation, and Testing." Econometrica (March) pp. 251-276.
Feldstein, Martin. 1992. "The Budget and Trade Deficits Aren't Really
Twins." Working Paper #3966, National Bureau of Economic
Research.
Hodrick, R. 1978. "An Empirical Analysis of the Monetary Approach
to the Determination of the Exchange Rate." In The Economics of
Exchange Rates: Selected Studies, ed. Jacob Frankel and Harry
Johnson. Reading, Mass.: Addison-Wesley Publishing Co., Inc.
Hooper, Peter, and John Morton. 1982. "Fluctuations in the Dollar: A
Model of Nominal and Real Exchange Rate Detennination."
Journal ofInternational Money and Finance (1) pp. 39-56.
Hutchison, Michael M., and Adrian W Throop. 1985. "U.S. Budget
Deficits fu.d the Real Value of the Dollar, " Federal Reserve BarIk of
San Francisco Economic Review (Fall) pp. 26-43.
Johansen, S~ren. 1988. "Statistical Analyses of Cointegration Vectors." Journal ofEconomic Dynamics and Control, pp. 231-254.
_ _ _ _ _ , and Katarina Juselius. 1990. "Maximum Likelihood
Estimation and Inference on Cointegration-With Applications to
the Demand for Money." Oxford Bulletin of Economics and
Statistics (May) pp. 169-210.
Koedijk, Kees G., and Peter Schotman. 1990. "How to Beat the
Random Walk: An Empirical Model of Real Exchange Rates."
Journal of International Economics pp. 311-332.
Krugman, Paul. 1983. "Oil and the Dollar." In Interdependence and
Flexible Exchange Rates, eds. Jagdeep S. Bhandari and Bluford
Putnam. Cambridge, Massachusetts: The MIT Press.
Meese, Richard. 1990. "Currency Fluctuations in the Post-Bretton
Woods Era." The Journal of Economic Perspectives (Winter)
pp. 117-134.
_ _ _ _ _ , and Kenneth Rogoff. 1983a. "Empirical Exchange Rate
Models of the Seventies: Do They Fit Out of Sample?" Journal of
International Economics pp. 3-24.
_ _ _ _ , and
. 1983b."The Out-Of-Sample Failure
of Empirical Exchange Rate Models: Sampling Error or Misspecification?" In Exchange Rates and International Macroeconomics, ed. Jacob Frankel. Chicago: NBER and University of
Chicago Press.
_ _ _- , and
. 1988. "Was It Real? The Exchange
Rate-Interest Differential Relation Over the Modem Floating-Rate
Period." The Journal of Finance (September) pp. 933-948.
Price, WR., and Patrice Muller. 1984. "Structural Budget Indicators
and the Interpretation of Fiscal Stance in OECD Countries."
OECD Economic Studies (Autunm) pp. 27-72.
Shafer, Jeffrey R., and Bonnie E. Loopesko. 1983. "Floating Exchange
Rates after Ten Years." Brookings Papers on Economic Activity (1)
pp.I-70.
Throop, Adrian W. 1989. "Fiscal Policy, the Dollar, and International
Trade: A Synthesis of Two Views." Federal Reserve Bank of San
Francisco, Economic Review (Summer) pp. 27-44.
Tobin, James. 1980. Asset Accumulation and Economic Activity. Chicago: University of Chicago Press.
Trehan, Bharat. 1986. "Oil Prices, Exchange Rates, and the U.S.
Economy." Federal Reserve Bank of San Francisco Economic
Review (Fall) pp. 25-43.
Wolff, C.C.P. 1987. "Time-Varying Parameters and the Out-of-Sample
Forecasting Perfonnance of Structural Exchange Rate Models."
Journal of Business and Economic Statistics pp. 87-98.
Determinants of Bank Versus Nonbank
Competitiveness in Short-term Business Lending
Elizabeth S. Laderman
Economist, Federal Reserve Bank of San Francisco. The
author would like to thank Timothy Cogley, Brian Cromwell, Fred Furlong, Chan Guk Huh, John Judd, Ramon
Moreno, Jonathan Neuberger, Randall Pozdena, Ronald
Schmidt, and Bharat Trehan for helpful suggestions and
encouragement, and Deborah Martin and Deanna Brock
for excellent research assistance.
Since about 1974, bankS share ofthe marketfor short-term
business lending has been steadily eroded through competition with nonbank creditors. This paper tries to identify
some factors that may affect bank competitiveness in this
category in the short run and discusses how these factors
may have contributed to banks loss of market share.
Estimation of a simple linear model in first differences
indicates that banks market share responds negatively in
the short run to above-average default risk and/or monetary tightness and to a decrease in banks value ofdeposit
insurance. Banks market share responds positively to an
increase in the level of interbank competition. Extrapolation from the short-run model to long-run effects demonstrates the plausibility that above average risk and/
or monetary tightness and increases in the aggregate
weighted capital-to-assets ratio, which contributes to decreases in the value ofdeposit insurance, may have played
a small role in banks loss of market share since the
mid-1970s.
Since about 1974, banks' share of the market for short-term
business lending has been steadily eroded through competition with a variety of alternative creditors, including
finance companies and the commercial paper market. This
paper tries to identify some factors that may affect bank
competitiveness in this market, at least in the short run ,
with competitiveness being measured by banks' market
share of short-term business credit outstanding..
A reduced form model of bank market share is presented. In this model, market share over time depends on
four factors that theoretically could affect the relative
supply and demand for short-term bank business loans over
time, specifically: (1) changes in the overall level of risk in
the economy, (2) financial innovation, (3) bank regulatory
costs and benefits, and (4) the level of interbank competition. On the basis of this model, a time series is estimated
using linear regression techniques.
Because the data fail stationarity and cointegration tests,
the model is estimated in first differences and only shortrun influences are identified. I find some evidence that
bank market share in short-term business lending responds
negatively in the short run to an increase in banks' aggregate weighted capital-to-assets ratio (taken as a proxy for
the value of deposit insurance) and positively to an increase
in the percentage of total bank assets that is held by bank
holding companies headquartered in states with interstate
banking (taken as a proxy for the level of interbank
competition). In addition, banks' market share appears to
fall whenever the deviation is positive between the commercial paper-Treasury bill spread and that spread's
mean (taken as a proxy for either the level of economy-wide
default risk or the stance of monetary policy).
On the basis of the regression results, the paper then
discusses the possible roles played by the various independent variables in explaining the decline in bank market
share. However, because the lack of cointegration in the
model means that the explanatory variables cannot fully
account for the long-run influences on bank market share,
the conclusions offered are treated as tentative.
Tue paper is organized as follows: Section I contains
some background on banks' competitors in short-term
business lending. Section II discusses the factors that in
theory could affect bank competitiveness in short-term
business lending. Section III presents a reduced form
model of bank market share, defines variables, and discusses data and econometric issues. Section IV contains
18
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
regression results. Section V discusses the possible roles of
the various independent variables in explaining the decline
in bank competitiveness, and Section VI concludes.
I.
BANKS' COMPETITORS IN
SHORT-TERM BUSINESS FINANCE
In this study, bank competitiveness in short-term business
lending is measured by bank market share in this category, based on data from the Board of Governors' Flow of
Funds Accounts. As of the third quarter of 1992, bank
loans accounted for 52.6 percent of short-term credit to
nonfinancial business, "other intermediated credit" for
39.3 percent, and commercial paper for 8.1 percent.!
The Flow of Funds Accounts include several sources of
what is here called "other intermediated credit," and in
the following discussion, I will focus on the two largest,
namely, finance companies and offshore lenders. 2 Finance
companies, like commercial banks, are financial intermediaries. They raise funds in the commercial paper
market, often selling their paper to money market mutual
funds, and they lend to both businesses and individuals.
Because they do not take insured deposits, finance companies are not subject to regulations, such as reserve requirements and capital requirements, that apply to banks.
In this study, offshore credit may originate from foreign
banks or other entities that are located outside the United
States. 3 Domestic subsidiaries offoreign banks, as well as
U.S. agencies and branches offoreign banks, are excluded
from the definition of offshore lenders. Offshore lenders
are for the most part not subject to the same regulations that
face domestic banks.
The third source of short-term business credit, commercial paper, consists of unsecured short-term promissory
notes that are offered to investors either through dealers or
directly by the issuer. Original maturities of commercial
paper range from one to 270 days, but average less than 60
days. The commercial paper market is a direct debt mar-
1. Trade credit also is a potentially important source of short-term
business credit, but it is omitted from the total in this study. In addition,
here short-term credit generally has a maturity that is less than one year.
2. Using the Flow of Funds breakdown of "other loans" (called here
"other intermediated credit") for the nonfinancial corporate sector and
applying it to the entire nonfinancial business sector, I estimate, that,
as of year-end 1989, for example, finance company loans accounted for
54 percent of other intermediated credit to nonfinancial business. Other
sources and their shares were: offshore (14.5), U.S. government (13.4),
bankers' acceptances (7.4), savings and loans (6.7), and governmentsponsored agencies (4).
3. A small proportion of this credit is from offshore bookings of U.S.chartered banks.
ket, meaning that commercial paper credit is not intermediated. Most commercial paper is backed by bank lines
ofcredit and is therefore issued by those able to obtain such
lines of credit, that is, the most creditworthy borrowers. 4
Some commercial paper is sold indirectly through dealers. 5 Thus, the commercial paper market is regulated
indirectly by the SEC, which has the authority to issue
rules, such as capital requirements, that govern all securities dealers. The SEC also has regulatory authority
over some commercial paper investors, such as money
market mutual funds. However, any such regulations appear to have been relatively inconsequential to the commercial paper market during the period under study; they
receive little or no attention in discussions of the commercial paper market and in studies of competition between
banks and the commercial paper market. 6
II.
DETERMINANTS
OF BANK COMPETITIVENESS
In this section, I discuss variables which, in theory, could
affect bank competitiveness in short-term business lending. These variables represent aspects of the ultimate
determinants of bank competitiveness in short-term business lending: the supply and demand for short-term bank
loans relative to the supply and demand for alternative
short-term business financing. The variables relate to the
level of risk in the economy, the relative benefits to banks
versus other types of creditors of financial innovation,
bank regulatory costs and benefits, and the level of interbank competition. 7
Economists have several theories about the comparative
advantage banks have in serving higher-risk customers. For
4. According to Moody's, about 84 percent of the total commercial
paper issues worldwide are issued by companies with A-I ratings from
Standard and Poor's and P-I from Moody's. See Fuerbringer (1991).
5. Dealers take only about 5-10 percent of the commercial paper they
buy into their own inventories. They purchase the remainder for other
investors and typically charge a commission of about 1/10 to 1/8 percent
on an annual basis (Cook and Rowe 1986, p.1l6.)
6. See, for example, Estrella (1987), Hurley (1977), Judd (1979), and
Cook and Rowe (1986). Hurley does mention that dealers who take
relatively high-risk paper (i.e., when the issuer is unrated by two rating
services) into inventory have to hold more capital. This rule went into
effect in mid-1977, but would have been of very little consequence to the
commercial paper market as a whole, because the vast majority of
commercial paper is rated.
7. The list of explanatory variables used here is far from exhaustive. For
example, I have not considered variables that may affect a bank's choice
between making short-term business loans and making other types of
investments. However, the ratio of bank short-term business credit to
total bank loans and leases has changed relatively little between 1972
LADERMAN / BANK VERSUS NONBANK COMPETITIVENESS
example, Diamond (1984) pointed out that informationgathering and evaluation of borrowers and their projects is
more efficiently conducted once by a single intermediary,
such as a bank, than repeatedly by numerous individual
lenders. In addition, long-term relationships with borrowers can be a unique source of information. Such
relationships, by their nature, can only be maintained
between a borrower and at most a handful of lenders. This
comparative advantage in information-gathering and analysis presumably· enables intermediaries to make higherrisk loans than those that are made in the direct debt
market, because information may lower the effective risk
to the lender. This intuition is consistent with the observation that issuers of commercial paper tend to be quite lowrisk borrowers.
When intermediaries' information-gathering includes
monitoring borrowers' adherence to loan commitments,
their relative ability to serve higher-risk customers may be
even further enhanced. In this view, monitoring is an
additional form of information gathering. Diamond (1991)
shows that intermediaries who monitor are especially
valuable to borrowers with credit ratings toward the IPiddle
of the spectrum. In the presence of moral hazard (the
incentive that a borrower has to default on a loan because
his own money is not at risk), borrowers need to offer
potential lenders some assurance that they will not renege.
The highest-rated borrowers can credibly "stake their
reputation" on their promise to honor their obligationstheir good reputation is what allows them to raise capital at
a lower rate, and it must be maintained to retain this source
of higher profits. These high-rated borrowers do not need
to be monitored, and they access debt markets directly. In
contrast, very low-rated borrowers have little to lose ifthey
reveal bad news about themselves by defaulting, or by
being caught when monitored. Medium-risk borrowers are
the ones who need to be able to offer future direct lenders a
good "track record" of having been monitored and not
found wanting.
Among intermediaries, banks are thought to have special loan-monitoring capabilities, which may stem from
banks' special access to information, including information regarding transactions activity, gained from deposit
relationships with borrowers (Black 1975). In addition,
(the earliest year for which such data are easily available) and 1990,
ranging from around 30 to 34 percent. In addition, although increases in
off-balance-sheet activities may have contributed to a decline in all types
ofloans, Baer and Pavel (1988) attribute the growth of off-balance-sheet
activities partially to increases in banks' capital requirements, and this
is controlled for in the model presented here. I also have not attempted to
measure banks' cost of complying with numerous and varied consumer
regulations.
19
even if banks do not lend to their own depositors, their
general deposit relationships are likely to yield information
about the state of local and regional economies that might
be useful in monitoring. For example, a banker may assess
a local customer's ability to meet loan commitments differently depending on whether or not that customer's industry
is experiencing a downturn in the area. Banks may be able
to glean advance knowledge of such a downturn from, for
example, changes in the inflow and outflow of transactions
deposits. 8
The fact that banks monitor and may thereby have a
comparative advantage in making risky loans indicates that
the general level of risk in the economy may influence the
relative demand for bank credit. However, the direction of
influence can be positive or negative. For example, Diamond's model suggests that, as the general level of risk
increases, more borrowers in the lowest-risk category may
opt for bank credit, but, at the same time, the bank may lose
more borrowers to the highest-risk category than it gained
from the lowest-risk category.
The next factor to be considered is financial innovation.
Here, it is suggested that the financial innovations that
began in the 1970s may have put banks at a disadvantage
relative to other lenders and therefore may have affected
bank competitiveness in the period under study.
Any disadvantage stemming from financial innovation
would have resulted largely from regulations that banks
face but other lenders do not. In particular, the development and subsequent growth of money market mutual
funds, beginning in the early 1970s, caused an outflow of
savings from banks. This is because bank time and savings
accounts had legally mandated ceilings on interest rates,
and money market mutual funds offered a way to circumvent these restrictions. Thus, money market mutual fund
growth accelerated significantly in 1973 and 1974, when
market rates increased to new highs, and also in later
periods of high rates.
Whether savings outflows into money market mutual
funds actually decrease banks' short-term businesslending
is somewhat debatable. One reason to suspect that they may
not is that such outflows affect only a subset of funding
sources. Banks can obtain funding from sources other than
retail deposits (including money market mutual funds
themselves, which may purchase bank certificates of deposit) and may therefore substitute purchased funds for
8. See Fama (1985) for a model in which banks have special monitoring
capabilities and in which bank borrowing is useful to borrowers because
it generates information which is useful to the borrower's other potential
lenders. James (1987) also has found empirical evidence that the
information generated by bank loans is useful to borrowers as a signal of
creditworthiness.
20
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
retail deposits. In addition, banks can shift asset portfolios, and therefore there is no a priori reason to believe that
a reduction in funding affects short-term business credit in
particular. However, despite the fungible nature of bank
liabilities and assets, the development of money market
funds may indeed curtail bank competitiveness in shortterm business lending. In addition, other financial innovations, such as advances in corporate cash management
techniques and greater use of security repurchase agreements, which both reduce the demand for demand deposits, also may affect banks' short-term business lending.
Moreover, the growth of money market mutual funds in .
particular may affect bank competitiveness not only by way
of savings outflows, but also by way of a beneficial
synergistic effect on the competitiveness of finance companies and the commercial paper market. This is because
the largest single category of money market mutual fund
investments is commercial paper, and finance companies
raise most of their funds through the commercial paper
market, selling mainly to money market mutual funds. 9
The next few variables to be considered as influences on
bank competitiveness also derive their potential importance from the unique regulation of banks. First, banks
face reserve requirements-that is, they must hold noninterest bearing deposits at the central bank-while their
competitors do not. Reserve requirements therefore impose
a cost on banks relative to their competitors, who may
optimally invest all their funds. However, as with savings
outflows, whether a general increase in required reserves
affects banks' business lending in particular might be
questioned. The implicit tax on banks might be passed on
mainly to banks' borrowers (but even then perhaps mostly
to non-business customers) in the form of higher interest
rates on loans, thereby decreasing the equilibrium supply
of loans, but it might be passed on mainly to depositors in
the form of lower interest rates on deposits, thereby having
little effect on bank credit. 10 In Section IV, I will investigate empirically whether reserve requirements really do
affect banks' share of business credit.
Second, banks have access to deposit insurance, while
their competitors do not. Deposit insurance benefits banks
because it allows them to raise funds at risk-free interest
rates, no matter how risky their loans. As Merton (1977) has
shown, banks hold a put option in the form of deposit
9. As of 1990, 48.4 percent of money market mutual fund assets
consisted of commercial paper (Post 1992).
10. See Black (1975) for a model in which the interest foregone on
reserves is passed on in the form of lower interest for depositors. See
Farna (1985) for a model in which the reserve tax on certificates of
deposit is borne by bank borrowers.
insurance that, all other things equal, increases the profitability of high-risk loans by allowing banks to reap a high
payoff if they "win," while letting the insurer payoff
depositors if they" lose." However, banks do pay premiums
for deposit insurance. Various au.thors have attempted to
determine whether, in practice, the net value of deposit
insurance to banks is positive or negative, with inconclusive
results. For example, Pennacchi (1987) concluded that the
answer depends on the degree of the insurance authority's
regulatory control over banks. However, whether deposit
insurance has, on net, a positive or negative value, we can
say that an increase in the value should increase banks'
returns, enhance their ability to attract funds, and thereby
increase their relative supply ofcredit. Likewise, a decrease
in the value of deposit insurance would be expected to
decrease banks' relative supply of credit.
Third, over much of the period under study, Regulation
Q ceilings on bank interest rates were in effect, and these
may have played a role in bank competitiveness, complementary to but separate from their role as spurs to innovation. As mentioned above, an .important feature of the
economy in the early to mid-1970s was the increase in
short-term interest rates in the face of ceilings on consumer
deposit rates at commercial banks. Even if increases in
spreads between market rates and deposit ceilings had not
encouraged the development of money market mutual
funds, they still likely would have increased disintermediation, that is, the flow of funds out of banks and into
whatever higher yielding market assets existed at the time.
Fourth, market structure.in the banking industry has
likely changed due to the liberalization of interstate banking. Laderman and Pozdena (1991) found that the liberalization of interstate banking laws tends to increase the
competitiveness of banking markets, as new opportunities
open up for competitors to enter from out-of-state. Consequently, because output and total revenue are greater under
perfect competition than under monopoly, interstate banking may increase banks' total dollar value of assets. Therefore, assuming no change in the level of competition within
other sectors of the short-term business credit market, it is
reasonable to suppose that the liberalization of interstate
banking may increase bank competitiveness in short-term
business lending. 11
11. Because I am viewing banks as competing with other intermedialies
and the direct debt market, I do not actually think of any bank as having
as much market power as a monopolist would have. Even if it is the only
bank in the area, any bank would have competitors in the form of other
types of lenders. Despite this, it still is reasonable to examine the effect
of bank market structure on aggregate bank credit, because banks
remain the major providers in several areas, including payments system
services, demand deposits, and short-term business credit itself.
LADERMAN /
m. MODEL OF BANKS' SHARE
OF SHORT-TERM BUSINESS CREDIT
In this section, I present a model of bank competitiveness
in short-term business lending. This model is a function of
the factors that were discussed in Section II, and it uses the
following empirical measures for these factors.
RISK = 6-month commercial paper interest rate
- 6-month Treasury bill interest rate
- mean of this difference over the
period 1960-1990;
TBHIGH = highest 6-month Treasury bill interest
rate to date;
RESREQ = (aggregate required reserves/total bank
assets) x 6-month Treasury bill interest
rate;
KARATIO = total bank capital/total bank assets;
PREM = net aggregate deposit insurance assessments/total insured deposits;
SPREAD = difference between 3-month Treasury bill
interest rate and ceiling on interest rate
on savings deposits, if this difference is
positive, zero otherwise;
and
INTERST = percentage of total bank assets held by
bank holding companies that are headquartered in states that permit interstate
banking.
RISK· measures the level of risk in the economy. Since
the commercial paper interest rate reflects a default risk,
while the •interest rate on Treasury bills is essentially
risk-free, the spread between the two rates may be an
indicator of overall risk in the economy (Friedman and
Kuttner 1991).12Iuse the deviation of the paper-bill spread
from its mean to measure the level of risk relative to a
"normal" level of risk for the period. It should be noted
that the paper-bill spread··also can be interpreted as· a
BANK VERSUS NONBANK COMPETITIVENESS
21
measure ofthe tightness of monetary policy, with a higher
spread indicating greater tightness. It is expected that
monetary policy tightening would differentially affect bank
lending, lowering banks' share of short-term business
credit.!3
It may also be noted that, as a measure of default risk,
the effect of an increase in this variable on bank market
share is expected to be either positive or negative, as
explained in Section II. On the other hand, as a measure of
the tightness of monetary policy, its effect is expected to be
unambiguously negative.
TBHIGH measures financial innovations. Rather than
attempt to measure particular financial innovations directly, I use a variable that other researchers have used as an
indicator of the incentive for financial innovations, because
it should serve well as a general measure of all such
innovations, not just particular innovations. For example,
financial innovations like money market mutual funds,
security repurchase agreements, and cash management
methods become more attractive as interest rates rise.
For consumers, the spread between market interest rates
afJ.d ceilings on bank rates rises with market rates, creating
a market for money market mutual funds. For businesses,
the spread between market rates and bank deposit rates
is positive and also tends to rise with market interest
rates, creating a market for ways to economize on transactions balances, such as repurchase agreements and cash
management.
So, increases in market interest rates may increase the
profitability of investing in financial innovations. However,
if there is a fixed cost to such innovation (for example, the
cost of training staff to manage repurchase agreements or
the writing of mutual fund management software), it will
not be undertaken unless the present discounted value of
the interest gained thereby is at least as high as the fixed
cost.
Previous authors, for example, Enzler, Johnson, and
Paulus (1976), have suggested using the previous highest
interest rate as a measure of the perceived net profitability
of financial innovation. The idea here is that only if interest
rates rise to unprecedented levels will firms perceive that
the high rates will persist long enough to make the benefits
of innovation outweigh the costs. In addition, subsequent
12. As Friedman and Kuttner point out, even if the actual incidence of
default by commercial paper issuers is relatively rare, the paper-bill
spread still may be a satisfactory gauge of the perceived level of overall
default risk. One reason simply may be that subjective probabilities of
default, even if rational, may not equal the frequency rate of default
observed within any finite time period. Another possibility is that
subjective probabilities are not in fact rational. Because it is subjective
default probabilities that matter in the context of the explanation given
above for the potential importance of risk, the paper-bill spread would
seem to be satisfactory.
13. Friedman and Kuttner (1991), in contrast, argue that changes in
bank loans relative to total credit resulting from a tightening of monetary
policy cause changes in the paper-bill spread. In the model presented
here, monetary policy would simultaneously raise the paper-bill spread
(as well as other interest rate spreads, such as the bank loan-bill spread
and the certificate of deposit-bill spread) and lower banks' relative
supply of business credit.
22
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
reductions in interest rates will not reverse the process,
because the innovations already will be in place. 14
RESREQ measures the cost of reserve requirements.
Aggregate required reserves is multiplied by a nominal
interest rate, the 6-month Treasury bill rate, because required reserves pay no interest. This means that the opportunity cost that banks face as a result of having to hold such
reserves rises with the return that would be earned were
such reserves not required. The reserve requirement variable has total bank assets in the denominator as a scaling
factor.
KARATIO and PREM measure changes in the value of
deposit insurance. Deposit insurance can be thought of as a
put option, and, as shown by Merton (1977), its value to the
bank depends negatively on the bank's capital-to-assets
ratio. I measure this ratio with the aggregate book value of
capital divided by the aggregate book value of assets,
which equals the weighted average of individual bank
capital-to-assets ratios, where the weight is the ratio of that
bank's assets to total bank assets. 15 PREM, which controls
for banks' cost of deposit insurance, measures premium
assessments, net of credits, per dollar of insured deposits.
(Up until the early 1980s, the Federal Deposit Insurance
Corporation refunded to banks a portion of assessment
income at the end of each year.)
SPREAD measures the level of disintermediation. The
3-month Treasury bill rate is used rather than the 6-month
rate because the 3-month bill is the more liquid of the two
instruments. The same reasoning applies to the use of the
ceiling on savings deposit interest rates rather than the ceiling on rates on bank certificates of deposit. 16
14. Enzler, Johnson, and Paulus (1976) use previous peak interest rates
rather than peak interest rates to date, presumably because they see this
variable as working with a lag. I include in my regression eight lags on
the highest interest rate to date.
15. The capital-to-assets ratio may also affect bank competitiveness
through its effect on banks' tax burden. Because debt is generally
favored in the tax structure, an increase in the capital-to-assets ratio
tends to increase taxes, and thereby, all other things equal, impair bank
competitiveness relative to other creditors. It is also possible that an
increase in regulatory capital minimums is at least partially an indicator
of an increase in omitted factors that have reduced bank profitability and
competitiveness. In other words, regulators may have increased required capital-to-assets ratios in response to deteriorating bank health.
However, this explanation of the effect of capital ratios is more plausible
for the late 1980s than forthe period under study as a whole.
16. I use the savings deposit interest rate ceiling even after December
1982, when money market deposit accounts (MMDAs) were introduced
at banks. Even though MMDAs were not in general subject to a ceiling
on interest rates, ceilings did apply until January 1986 for accounts that
maintained an average balance of less than $2,500. However, see
Furlong (1983) for an account of the instant popularity of MMDAs,
despite this restriction.
INTERST measures interstate banking. I? As a measure
of interbank competition, the interstate banking variable is
preferable to other variables such as concentration. ratios
because it is more of an underlying driving force. For
example, the concentration ratio in a local or regional
banking market may fall in response to the passage of
liberalized interstate banking laws. One could say that it
is the change in concentration that affects competition, but
the real driving force is the change in laws. In addition, the
measure used here allows for competition to be affected by
the mere threat of entry, whereas concentration measures
do not. 18 Finally, interstate banking was found to be
correlated with higher levels of interbank competition
(Laderman and Pozdena 1991).
The Model
Let nominal short-term business loans from banks, L b , and
other nominal short-term business credit, L o ' be exponential functions such that
(la)
Lb = exp (c b
+ "ht +
f3~X
+
L o = exp
+
I3~X
+
Eb )
and
(lb)
(co
'Yo
t
+
Eo),
where Cb and Co are constants, 'Yb and 'Yo are coefficients, t
is a time trend, f3b and 130 are vectors of coefficients, X is a
vector of the seven explanatory variables, and Eb and Eo are
error terms.
Because the dependent variable is banks' share of shortterm business credit, it has a value that is restricted to be
between zero and one. The error term in the ordinary least
squares linear regression model takes on values between
negative infinity and infinity, so it is necessary to transform
the dependent variable so that it has the same range. A customary transformation, the logistic transform, maps (0, 1)
symmetrically into ( - 00, 00). The logistic transform of the
share, s, is S, where
17. These data are from the Compustat bank file, which contains
headquarters location and asset data for a sample of about 150 leading
U.S. bank holding companies, representing about 80 percent of U.S.
bank assets.
18. It is possible that the interstate banking variable is endogenous;
states with weak banks may pass interstate banking laws with the hopes
of increasing the market values of their banks as potential acquisition
targets. However, this may not be a significant concern, because on
average, the liberalization of interstate banking laws decreases bank
stock returns (Laderman andPozdena 1991). Nevertheless, some concern remains that the type of effect described might impart a negative
bias to the coefficient on the interstate banking variable.
LADERMAN /
BANK VERSUS
NONBANK COMPETITIVENESS
23
FIGURE 1
S = logh ~s)'
BANKS' SHARE OF SHORT-TERM BUSINESS loANS
Letting L T be total short-term business credit, we have,
from (la) and (Ib),
Lb
(2)
log ( ~)
1 s
=
T
log ( L
Lo
LT
= cb -
)
Co
=
Percent
log ( ~ )
Lo
+ C"h -
'Yo)
OUTSTANDING
t
Simplifying,
(3)
S = c
+ 'Y t + WX +
E,
with the coefficients and the error term in (3) corresponding to the differences between the coefficients and the error
terms, respectively, in the underlying equations (Ia) and
(Ib). Thus, the coefficients in (3) show the response of
outstanding nominal short-term bank credit relative to the
response of nominal other credit to changes in the explana-
60
65
70
75
80
85
90
tory variables.
Data
The model is estimated using quarterly data from the Board
of Governors' Flow of Funds Accounts for the first quarter of 1960 through the end of 1990. 19 Aggregate required
reserves, total assets, and capital are measured in billions of current dollars and were obtained from the Board
of Governors' Annual Statistical Digest. 2o The 6-month
Treasury bill rate, the 3-month Treasury bill rate, the
ceiling rate on savings deposits, ,!-nd the 6-month commercial paper rate all are in percentage terms.
The nontransformed version of banks' share of total
U.S. short-term nonfinancial business debt outstanding is
shown in Figure 1. The most striking feature is the steady
decline in banks' share that begins in about 1974. However,
19. The numerator of the underlying (nontransformed) share variable is
labeled "bank loans not elsewhere classified" in the total credit outstanding to nonfinancial business schedule of the Flow of Funds. The
majority of bank loans to nonfinancial business that are "elsewhere
classified" are real estate loans. The denominator is bank loans not
elsewhere classified plus "other loans" (which excludes real estate
loans) plus "commercial paper."
20. The construction of the data series for KARATIO involved splicing
together capital and assets measures from two sets of data series-one
for domestic offices of domestically chartered banks CRCON") from
1960.Ql to 1969.Ql and the other for domestic and foreign offices of
domestically chartered banks ("RCFD") from 1969.Q2 to 1990.Q4-
the goals of this paper are, first, to explain only short-run
variations in bank competitiveness and then to use these
results to explore only informally the possible reasons for
the long-run decline. Thus, Figure 1 mainly provides a
basis for subsequent discussion of econometric and measurement issues. (For the interested reader, plots of each of
the explanatory variables are included in the Appendix.)
As shown in Table 1, the dependent variable, TSHARE
(S in equation (3)), and all but one, or possibly two, of the
explanatory variables have unit roots and are thus nonstationary. The variable RISK stands out as the one definitely
stationary variable, while the reserve requirement variable
mayor may not be stationary. However, the treatment of
RESREQ as stationary or nonstationary did not affect the
results.
because the RCON set changed the definition of capital in 1983, and the
RCFD set only began in 1969.
This is a reasonable approach because U.S. banks had very few
foreign operations in 1969, and consequently, the changes in the spliced
series at the crossover points are very small-only 21 percent of the
standard deviation of ReFD assets (calculated over the subsequent three
years), and only 14 percent of the standard deviation of RCFD capital,
for assets and capital, respectively.
The variables KARATIO and RESREQ use this spliced asset series
(based on data from the Annual Statistical Digest), while the variable
INTERST uses the total assets of banks included in the Compustat
sample.
24
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
TABLE 1
TESTS FOR UNIT ROOTS
(AUGMENTED DICKEy-FUlLER TESTS)
VARIABLE
LEVELS a
FiRST DIFFERENCES b
TSHARE
-1.99
RISK
-4.04***
TBHIGH
-2.28
-3.53***
RESREQ
- 3.23*
-4.83***
KARATIO
-0.87
-3.91***
PREM
-0.51
- 3.57***
SPREAD
-1.95
-3.68***
INTERST
-0.92
-3.83***
ness credit booked offshore. Ifthis is true, then the dependent variable is an overestimate of bank share as defined,
and regression results could be misleading. However, as
will be discussed below, results using the McCauley and
Seth measures of offshore loans are qualitatively similar to
those obtained using the Flow of Funds data.
-3.52***
!'-1/A
*Reject null hypothesis (unit root) at 10% level.
***Reject null hypothesis at 1% level.
aWith constant and trend, 119 observations. Critical values for 100
observations: -4.04 (1%), -3.45 (5%), -3.15 (10%).
bWith constant, 118 observations. Critical values for 100 observations: -3.51 (1%), -2.89 (5%), -2.58 (10%).
In the presence of nonstationarity, the normal procedures
of statistical inference for ordinary least squares regression
are invalid. There are two possible remedies. One is to find
a cointegrating relationship between the nonstationary variables in the equation. The other is to estimate the equation
in first difference form, transforming each nonstationary
variable into the difference between itself and itself lagged
one period.
Despite several explanatory variables with strong trends
(one indication of the possibility of causal relationships
between levels of the explanatory variables and the level of
the dependent variable), statistical tests showed that the
dependent variable is not cointegrated with the set of nonstationary explanatory variables (whether or not RESREQ
is included in this set). Also, the dependent variable is not
cointegrated with any of the individual nonstationary
explanatory variables. 21
A final data issue pertains to the dependent variable.
McCauley and Seth (1992) have argued that the Flow of
Funds data significantly underestimate the volume of busi-
21. A residual-based test for cointegration was used. TSHARE was
regressed on a constant, a time trend, and the levels of all of the explanatory variables except RISK. Then, a unit root test was performed on the
residual from this regression, with four lags on the first difference of
the residual· in the unit root regression. Critical values were obtained
from Table llc in Phillips and Ouliaris (1990).
IV
REGRESSION RESULTS
Results Using Flow ofFunds Measure
of Offshore Loans
Given the lack ofcointegration, equation (3) was estimated
using the first differences of the variables TSHARE,
TBHIGH, RESREQ, KARATIO, PREM, SPREAD, and
INTERST, and the level of the RISK variable. Note that the
time trend appears as a constant in the model in first
difference form.
It is reasonable to suppose that, if bank market share
responds to any of the explanatory variables, it is likely to
be only with a lag. Therefore, lags were applied to the explanatory variables, and no contemporaneous terms were
included on the right-hand side of the regression. In
addition, because the dependent variable could have its
own important dynamics, lagged values of the dependent
variable were included as explanatory variables. To economize on degrees of freedom and simultaneously pick the
lag lengths, the model was estimated using a final prediction error (FPE) procedure, with the possibility of up to
eight lags on each explanatory variable. The FPE technique essentially selects the variables and the number of
lags on those variables to minimize the model's prediction
error.
The regression results are presented in Table 2. 22 Note
that the FPE procedure did not select three of the variables
at all: TBHIGH, RESREQ, and PREM. (RESREQ was not
selected whether it was included in levels or first difference
form.) Apparently, changes in these variables, representing
financial innovations, reserve requirements, and deposit
insurance premiums, respectively, do not aid in predicting
changes in banks' share of short-term business lending. As
discussed in Section II, it may not be surprising that
TBHIGH and RESREQ do not appear to affect banks' share
of short-term business credit in particular. In light of
uncertainty regarding the incidence of reserve requirement
22. Two common diagnostic tests, a general Lagrange Multiplier (LM)
test for autocorrelation of the errors, and an Autoregressive Conditional
Heteroskedasticity (ARCH) test for heteroskedasticity of the errors,
found that the null hypotheses of no autocorrelation and no heteroskedasticity could not be rejected, lending further credence to the
results presented in Table 2.
LADERMAN / BANK VERSUS NONBANK COMPETITIVENESS
costs, an alternative measure, a variable based on the
reserve requirement ratio for certificates of deposit, was
substituted for RESREQ in the FPE regression (following a
determination that the new variable was nonstationary).23
However, the FPE procedure also did not choose this alternative measure.
Two of the included variables, the lagged dependent
variable and SPREAD, have negative coefficients at some
lags and positive coefficients at others. As indicated by the
sums of the coefficients, the net effect after two years of
both of these variables appears to be positive. Whether
these net effects are statistically significant is debatable.
However, F tests point toward the lagged dependent variable and the SPREAD being of some importance; F tests
indicate that the entire group of coefficients on the lagged
dependent variable likely is statistically significant and that
the group of coefficients on the SPREAD variable likely
also is statistically significant.
The importance and direction of influence of RISK,
KARATIO, and INTERST are easier to interpret. These
three variables have consistent signs on their lags, and the
net effects all appear to be statistically significant.
Recall that RISK enters the regression in levels form.
Therefore, the results show that when the risk premium is
above its mean, the change in banks' market share over the
course of the following period is negative, all other variables held constant in levels. Furthermore, an increase in
an above-average risk premium strengthens the subsequent
period's decrease in market share. On the other hand, an
increase in interbank competition appears to increase
banks' market share in the next period, as predicted. The
capital-to-assets ratio enters with four lags, with a negative
coefficient and a fairly high t statistic on each of them. Not
surprisingly, the t statistic for the sum also is high. As
predicted then, increases in banks' aggregate capital-toassets ratio seem to erode bank competitiveness, at least in
the short run.
A priori, the sign on the risk variable was ambiguous.
An explanation for this was that banks tend to serve
customers with a medium absolute amount of risk, so that,
as the general level of risk rises, the net inflow into banks'
pool of customers may be positive or negative. Apparently,
neither result obtains; instead, it appears that as long asthe
risk premium is simply above its average, banks tend to
23. 1\'10 versions of the variable were tried. One was the reserve
requirement ratio for certificates of deposit with a denomination of at
least $100,000 and a maturity of less than 90 days. The second was the
same ratio multiplied by the 6-month Treasury bill interest rate. These
alternatives to RESREQ were chosen followingFama (1985), which
showed that reserve requirement costs for certificates of deposit are
passed on to borrowers.
25
TABLE 2
FPE REGRESSION RESULTS
TRANSFORMED BANK SHARE OF SHORT-TERM CREDIT
EXPLANATORY
VARIABLE
Lag
Coefficient
t-ratio
Constant
0
-0.0093
-3.4785
TSHARE
1
2
-0.0451
0.1802
0.0365
0.0279
0.1977
0.1017
-0.0147
-0.2821
0.2021
-0.4728
2.0046
0.3964
0.3100
2.3018
1.1306
-0.1715
-3.3478
1.1055
-0.0159
-2.9771
3
4
5
6
7
8
Sum
RISK
KARATIO
1
2
3
4
Sum
-3.7615
-2.7047
-3.1887
-3.3731
-13.0280
-2.2272
-1.6682
~ 1.9449
-2.0883
-2.8388
SPREAD
1
2
3
4
5
6
7
8
Sum
-0.0052
-0.0069
0.0066
-0.0042
0.0067
0.0021
0.0062
-0.0017
0.0034
-1.8652
-2.5331
2.1711
-1.3372
2.0993
0.6615
1.9893
-0.5739
0.4534
INTERST
0.0013
2.2679
AdjustedR2
0.3461
Total observations
115
NOTE: All variables except RISK in first difference form.
lose market share in short-term business lending, all other
things equal. Again, though, it must be pointed out that the
proxy used to measure economy-wide default risk could
instead be a proxy for the tightness of monetary policy. It is
not unreasonable to suppose that when monetary policy is
tighter than average, banks lose market share in short-term
business lending.
The apparent statistical significance of the coefficient on
the constant term (as indicated by the size of the t statistic)
also should be noted. With the model in first-difference
form, the coefficient on the constant represents the simple
effect of the passage of time on the level of banks' market
share and is in that sense representative of the unexplained
portion of the general downward trend in that variable.
26
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
Results Using McCauley and Seth Measure
of Offshore Loans
McCauley and Seth (1992) have argued that the Flow of
Funds data significantly understate the volume of business
credit booked offshore. More specifically, they estimate
that from 1984 through 1991, offshore credit to U.S. businesses (mostly corporations) was actually more than dou-
ble the amount reported by the Flow of Funds, and ·the
discrepancy increased over time. If this is true, then the dependent variable is an overestimate of bank share as
defined, at least from 1984 on, and regression results could
be misleading. 24 To test the possible importance of this, I
substituted McCauley and Seth's measure of offshore loans
for the Flow of Funds measure of offshore loans and
recalculated the dependent variable for the years 1984
through 1990. 25 The qualitative results were the same as
those seen in Table 2. 26
V. BANKS' Loss OF COMPETITIVENESS
IN SHORT-TERM BUSINESS LENDING
because, although banks' short-term business credit increased in absolute terms, as shown in Figure 2, the two
other categories of short-term business credit increased at a
faster rate. As it turns out, other intermediaries' share of
short-term business loans outstanding increased 15 percentage points between 1974 and 1990, from 23.3 percent
to 38.3 percent, accounting for 73.9 percent of banks'
share decrease. So, for the most part, stronger growth in
other intermediated credit accounted for banks' loss of
market share in short-term nonfinancial business credit.
Only 26.1 percent of banks' share loss went to commercial
paper.
Given the steady decline in banks' market share, it is
natural to try to seek an explanation. For this purpose, I
will use the regression results presented in the last section,
but the shortcomings of that regression model must be
pointed out.
Specifically, because the model is estimated in first
difference form, there exists the possibility that, even if it
fits quite well, it is a very poor predictor of long-run
changes in the dependent variable. For example, a single
As seen in Figure 1, between 1974 and the end of
non-zero residual in one period in the first-difference
1990, banks' share of short-term business credit fell from
73.3 percent to 53 percent. This decline in share took place
equation means that the implied underlying level of the
dependent variable (calculated using a starting point and
accumulating one-period changes) will be off by the value
of the residual in all subsequent periods. Thus, using the
regression results to predict long-run changes in levels
from a fixed starting point, as opposed to predicting
period-to-period changes, can be misleading, and there is
no objective way to judge the extent of the error.
However, even though I have no estimated model of the
long-run change in banks' market share, I can use the estimated model of short-run changes to demonstrate informally the plausibility of certain explanations for banks'
market share decline. First, in Figure 3, Panel A, I use the
estimated model to predict the transformed level of bank
share, by using the level at the beginning of the period as a
starting point and then sequentially adding the sum to date
ofthe predictedperiod-to-periodchanges. Although the
predicted levels generally are too low during the period up
to 1972, the predicted series does a· fairly good job of
tracking the fluctuations in the actual series. Then, the
predicted series misses the briefsurge in 1972 that precedes the plunge in 1974, but seems to catch the plunge
itself.. Then,from 1976 through mid-1978 and from 1983
through 1990, the predicted series tracks the actualseries
fairly closely in leliels. Between mid-1978 and 1983, the
predicted level is off, but the changes are approximately
correct.
The implied levels model includes lagged values of the
dependent variable as explanatory variables, and the predicted series depends on these lagged values. The effect of
24. McCauley and Seth speculate that US. businesses miss large
amounts of offshore loans when reporting on the Treasury forms that the
Flow· of Funds uses, because they do not know that these loans are
booked offshore. McCauley and Seth therefore use data reported by
foreign banking authorities, which is based on reports filed by lenders,
who presumably have more accurate information than borrowers regarding the booking location. On the other hand, the definition of offshore
loans used by other central banks may not be strictly comparable with
the definition used in this paper; for example, some central banks may
report that their banks are lending to U.S.-based firms from non-US.
sites, when they are actually lending to foreign subsidiaries of US.owned firms.
25. Offshore credit to total nonfinancial business is not broken out
separately in the Flow of Funds tables, but offshore credit to nonfinan- .
cial corporate business is. Therefore, I assumed that all offshore loans
were made to corporations, and I simply subtracted the Flow of Funds
measure of offshore credit to corporations from total "other credit" to
nonfinancial businesses and then added the McCauley and Seth measure
to this category. I also assumed that, prior to 1984, the Flow of Funds
measure of offshore loans was roughly correct. The lack ofcomparable
information on offshore credit before 1984 leaves little alternative but to
make such an assumption if one wants to incorporate the McCauley and
Seth information. In addition, this approach is partially justified by the
relative lack of incentives for offshore booying prior to 1984.
26. As before, diagnostic tests for autocorrelation and heteroskedasticity were conducted.. The null hypothesis of no autocorrelation could not
be rejected. However, the ARCH test for heteroskedasticity indicated
that the null hypothesis of no first order autoregressive conditional
heteroskedasticity could be rejected at the 9 percent level .• While. this
result is not definitive, it does indicate that some caution should be used
in interpreting the t statistics reported in Table 2.
LAnERMAN / BANK VERSUS NONBANK COMPETmVENEss
FIGURE 2
SOURCES OF SHORT-TERM NONFINANCIAL
BUSINESS CREDIT
Billions of
1987 Dollars
700
600
500
400
300
... •
.
, .,
.'
••
.'
-,'
Other
200
Intennediaries
. ".
,. , '
....
......... '
.. -
100
'
"* '
Commercial
Paper
O~F'F'I'"""""""';:~~::;:::;:::;::;::;:::;::;:"""""""'-'-'-""""'~
60
65
70
75
80
85
90
lagged values of the dependent variable can be calculated
in two ways. One way uses the actual lagged values, as
shown in Panel A. The alternative, "dynamic forecasting," uses the lags of the predicted values of the dependent
variable. This more rigorous "test" of the model is shown
in Panel B. Judging by appearances, the dynamic forecasting procedure gives a picture that is roughly similar to the
one seen in Panel A.
Finally, Panel C presents a third test that is again
somewhat more rigorous than the last. Here, the dynamic
forecasting procedure is used, but the model is estimated
with data from 1960 through 1985 only. The fitted model is
then used for out-of-sample prediction of the level over the
period 1986 through 1990. Again, the model does fairly
well capturing changes in bank market share. The model
seems to have the most trouble from mid-1986 through the
third quarter of 1987. 27
All in all, though, the model seems to capture banks'
fall-off in market share fairly well. Therefore, it seems
reasonable to speculate that the same factors that appear to
contribute to negative first differences may also have contributed to the long, fairly steady decline seen since 1974.
27. Figure 3 uses the regression results based on the Flow of Funds
measure of offshore credit, rather than the McCauley and Seth measure.
Comparable results were obtained with the McCauley and Seth data.
27
To judge this, the difference between the predicted level at
the beginning of the period and the end of the period was
calculated, and the contributions of beginning- to end-ofperiod changes in the various explanatory variables to the
predicted decline were estimated.
As it turns out, the only explanatory variables that
changed so as to contribute to a decline in the dependent
variable were the lagged values of the dependent variable,
the time trend, and the risk premium. The simple effect of
the passage of time between 1962 and 1990 accounted for
86.1 percent of the total predicted decline, this total being
the sum of the effects of only those variables that contributed to a decline. (The sum of the negative predicted ef~
fects was - 1.25, while the net predicted change in the dependent variable, adding in the positive effects of the
changes in the capital-to-assets ratio, the savings spread,
and the extent of interstate banking, was -0.88. The
actual change in the dependent variable between the first
quarter of 1962 and the fourth quarter of 1990 was - 0.86.)
The change in the lagged dependent variable accounted for
13 percent, and the risk premium accounted for 0.9 percent. Attributing the effect of the change in the lagged
dependent variable proportionally to the time trend and the
risk premium, the passage oftime accounted for close to 99
percent of the decrease, while the change in the risk
premium accounted for about 1 percent of the decline. 28
However, if we restrict our attention to the period after
1973, a slightly different picture emerges. The capital-toassets ratio started to increase around 1974. (See Figure
A4 in the Appendix.) The increase was at first rather sharp,
then reversed itself in the second halfof the 1970s, and then
resumed around 1980. Given the negative coefficients on
the capital-to-assets ratio, the net increase in the capitalto-assets ratio since 1974 must have contributed to the
decline in banks' share since then. However, the part that
the change in the capital-to-assets ratio played still was
relatively small; the change in the capital-to-assets ratio
alone accounted for 7.3 percent of the estimated decline.
The other factors in the decline, the passage of time, the
lagged dependent variable, and the risk variable, accounted
for 68.4 percent, 21.5 percent, and 2.9 percent of the
predicted decline, respectively. (In the 1974 to 1990 period,
the sum of the negative predicted effects was -0.93,
while the net predicted change in the dependent variable,
adding in the positive effects of the other variables, was
28. This assumes that the model is correctly specified in that no
explanatory variables have been omitted. Because this is open to
question, the actual sizes of the effects of the passage of time and the
change in the risk premium likely are somewhat less than this, but still
greater than those calculated without attributing any of the effect of the
change in the lagged dependent variable to changes in those variables.
28
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
- 0.77. The actual change in the dependent variable
between the first quarter of 1974 and the fourth quarter of
1990 was -0.89.) Again, attributing the effect of the
change in the lagged dependent variable proportionally to
the other variables, the passage of time, the change in the
capital-to-assets ratio, and the change in the risk premium
accounted for about 87 percent, 9.3 percent, and 3.7 percent, respectively, of the decline in bank market share.
Given the apparent unimportance of changes in the
capital-to-assets ratio and of the risk premium, relative to
the importance of the unexplained effect of the time trend,
the model presented in this paper does not really "explain"
why banks' market share in short-term business lending
shrank over the past 30 years. However, among the factors
considered in this study, it is fair to say that the capitalto':assets ratio and the risk premium are the two variables
that are most likely to have played a part in that decline,
with the capital-to-assets ratio of slightly more importance
than the risk premium.
B:
ACCUMULATION OF PREDICTED
FIRST DIFFERENCES AND
DYNAMIC FORECASTING PROCEDURE
1.20
1.00
L
./\r...A
,~J..
-'.. ~, 0vvf'v\
.
Actual
TSHARE
~. ~,
0.80
Predicted",
TSHARE
''',
0.60
0.40
0.20
62 65 68 71
74 77 80 83 86 89
FIGURE 3
DEPENDENT VARIABLE PREDICTIONS
A:
ACCUMULATION OF PREDICTED
FIRST DIFFERENCES
OUT-OF-SAMPLE FORECAST OF
FIRST DIFFERENCES
1.20
0.40
Actual
TSHARE
1.00
0.80
c:
0.35
0.30
Predicted
TSHARE
,..
. --
,
.,I
,
0.60
0.25
;1
0.40
0.20
0.20
0.15
62 65 68 71
74 77 80 83 86 89
NOTE: In all three panels, TSHARE = log s/(l- s), where s =
(short-term bank business loans/total short-term business credit).
Actual
TSHARE
.,
Forecasted
TSHARE
,,
.. ,
.
0.10
86
87
88
NOTE: Based on 1962.Q2 to 1985.Q4.
89
90
29
LADERMAN / BANK VERSUS NONBANK COMPETITIVENESS
As discussed in Section II, an increase in its capital-toassets ratio decreases the value of a bank's subsidy from
deposit insurance and thereby reduces the bank's profitability and competitiveness. Put another way, a bank with
a higher capital-to-assets ratio must compensate for the
loss in subsidy in some way-for example, by charging
higher loan rates, reducing services to borrowers and/or
depositors, or paying lower deposit rates-to maintain an
adequate return on equity. But this strategy cannot be
followed for long without the bank losing market share to
creditors offering more favorable terms.
Whether a general increase in the capital-to-assets ratio
is in fact an important element of the story of why banks
have become less and less competitive in the provision of
short-term business credit since the mid-1970s remains
very open to question. If, however, increases in the capital~
to-assets ratio did have the effect that is being posited here,
it is important to point out that this study does not then
imply that required .capital-to-assets ratios for banks
should be 10wered.lfbankshave been receiving a positive
subsidy through deposit insurance, then it very likely is
desirable to raise capital requirements to eliminate that
subsidy and give banks the incentive to control their risktaking. Unless there are specific welfare or market failure
reasons for continuing to subsidize banks, a cutback in
subsidization is necessary, despite the possible effect on
bank competitiveness. Extreme caution must be used in
assessing the net effect of any decrease in capital requirements, given that such a move would likely increase the
public's potential deposit insurance liability.
Regarding the risk variable, whether this variable is in
fact representative of a risk premium or the stance of
monetary policy, it appears that, as long as it is above its
mean for the period, banks lose market share in short-term
business lending. As it turns out, over the 1962 to 1990
period for which the level of bank market share is simulated
(and over the 1974 to 1990 period), the sum of the aboveaverage values of the risk variable exceeds the sum of the
below-average values, so the overall effect of the risk
variable is to lower bank market share between 1962 and
1990 (and between 1974 and 1990).29 This is consistent
with the interpretation of the risk variable as representative
of either a risk premium or the stance of monetary policy.
An increase in the risk premium was predicted to have an
ambiguous effect on bank market share, while a tightening
of monetary policy was predicted to have a negative effect.
29. The mean of the risk variable was taken to be the mean for the entire
1960 through 1990 period. Therefore, had the contemporaneous instead
?fthe lagged value ofrisk been included in the model, and had the change
III bank market share between 1960 and 1990 been the focus, the risk
variable would have played no role. This is because the above-average
VI.
CONCLUSION
In this paper, I have attempted to identify some ofthe factors
that may affect bank competitiveness in short-term business
lending in the short run. The theoretical part of the paper
emphasized four general types of variables: the level of risk
in the economy, the relative benefits to banks versus other
types of creditors of financial innovation, bank regulatory
nrl "U\." ''', allU
{'<"'st"
~_.-:I +h- £ . -~e
't'
vv • ., au'" b"'''''''fi<n
U c; 1l e-·
Ve1l Ul
Hlt rOallK compeul0n.
After discussing the theoretical effects ofthese variables on
the supply and demand for short-term bank business credit
relative to other types of short-term business credit, empirical measures of these variables were introduced and a
simple linear model in first differences was presented and
estimated.
Estimation of the model yielded several interesting
conclusions. First, banks' market share in short-term business lending appears to respond to only some of the
theoretical variables that were considered. Among the variables considered, only the risk premium, the aggregate
weightedcapital-to-assets ratio, the spread between the
market interest rate and the deposit interest rate ceiling,
and the extent of interstate banking laws seem to matter in
the determination of short-run changes in banks' market
share. In addition to these, the mere passage of time plays
an important but non-illuminating role.
Second, banks' market share appears to fall in the short
run when the risk premium is above its long-run mean and
when the capital-to-assets ratio rises. On the other hand,
market share rises in the short run as the opportunities for
interstate banking become more widespread, in accordance with interstate banking being a proxy for the level of
interbank competition. The effect of an increase in the
interest rate spread is positive at some lags and negative at
others, but is positive on net after two years.
Third, the model in first differences was used to explore
informally the reasons for the steady decline in bank
1
1
.
values of risk, by construction, would have exactly offset the belowaverage values. However, because of the lag structure of the model, the
first period for which a level of bank share is simulated is the first quarter of 1962. This means that I examine the role of risk in explaining
the change in market share between only 1962 and 1990. Furthermore, the
first difference of the dependent variable depends on the first lag of
the risk variable. As a consequence ofthese factors, tti.e risk variable contributes to a 28-year decline in bank market share, largely because the
sum ofthe above-average values ofthe risk variable, summed over the 28
years over which the simulation is conducted, exceeds the sum of the
below-average values.
Separately, it is interesting to note that the large spike in the risk
variable occurs at precisely the same time that banks' market share began
to plunge. (See Figure AI in the appendix.)
30
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
competitiveness in short-term business lending since the
mid-1970s. After demonstrating that the estimated model
may shed some light on this issue, despite the lack of
cointegration, it was concluded that, among the variables
considered, only the capital-to-assets ratio and the risk
variable could have played any role. Although these variables "explain" only a small proportion of the decline,
their effects appear to be consistent with plausible theo-
retical explanations. First, the increase in the aggregate
capital-to-assets ratio since about 1974 may have contributed somewhat to the decline in bank competitiveness
by decreasing banks' deposit insurance subsidy. Second,
the above-average values of the risk variable since the
mid-1970s, representing either an unusually high economywide default risk premium or unusual tightness in monetary
policy (or both), also may have made a slight contribution.
ApPENDIX
The following are plots of the seven explanatory variables:
FIGURE Al
FIGUREA3
RISK
RESREQ
Percentage
Points
2.5
0.40
2.0
0.35
1.5
0.30
1.0
0.25
0.5
0.20
0.0
0.15
-0.5
-to
0.10
60
65
70
75
80
85
90
NOTE: RISK = 6-month commercial paper interest rate-6-month
Treasury bill interest rate-mean of this difference over the period
60
65
70
75
80
85
90
RESREQ = (aggregate required reserves/total bank assets)
X 6-month Treasury bill interest rate
NOTE:
1960-1990
FIGUREA2
FIGUREA4
TBHIGH
KARATIO
Percent
17.5
0.085
15.0
0.080
12.5
0.Q75
10.0
0.070
7.5
0.065
5.0
0.060
2.5
0.055
60
65
70
75
80
85
90
NOTE: TBHIGH = highest 6-month Treasury bill interest rate to date
60
65
70
75
80
85
90
NOTE: KARATIO = total bank capital/total bank assets
LADERMAN/BANK VERSUS NONBANK COMPETITIVENESS
FIGUREA5
FIGUREA7
PREM
INTERST
00015J
0.0013
I
0,0011
0.0009
0.0007
0.0005
0.0003
60
NOTE: PREM
65
=
70
75
80
85
90
net aggregate deposit insurance assessments/total
insured deposits
FIGUREA6
SPREAD
Percentage
Points
10.0
7.5
5,0
2,5
0.0 -\T..,...,."T"ri'rr""'''''rP'I'TTT"",''''''''''''''
00
E
ro ~
~
00
w
NOTE: SPREAD = difference between 3-month Treasury bill interest
rate and ceiling on interest rate on savings deposits, if this difference
is positive, zero otherwise
NOTE: INTERST = percentage of total bank assets held by bank
holding companies that are headquartered in states that permit
interstate banking
31
32
FRBSF EcONOMIC REVIEW 1993,
NUMBER
2
REFERENCES
Furlong, Frederick T. 1983. "New Deposit Instruments." Federal
Reserve Bulletin (May) pp. 319-326.
Baer, Herbert L. and Christine A. Pavel. 1988. "Does Regulation
Drive Innovation?" Federal Reserve Bank of Chicago Economic
Perspectives (MarchIApril) pp. 3-15.
Hurley, Evelyn M. 1977. "The Commercial Paper Market." Federal
Reserve Bulletin (June) pp. 525-536.
Black, Fischer. 1975. "Bank Funds Management in an Efficient
Market." Journal ofFinancial Economics 2, pp. 323-339.
Board of Governors of the Federal Reserve System. Annual Statistical Digest (various years). Washington, D.C.
_____ . Flow ofFunds Accounts (various years). Washington,
D.C. (Published as Statistical Release Z.1.)
Cook, Timothy Q., and Timothy D. Rowe, eds.1986. Instruments of
the Money Market, 6th ed. Federal Reserve Bank of Richmond.
Diamond, Douglas W. 1984. "Financial Intermediation and Delegated Monitoring." Review ofEconomic Studies 51, pp. 393-414.
_ _ _ _ _ . 1991. "Monitoring and Reputation: The Choice Between Bank Loans and Directly Placed Debt." Journal ofPolitical
Economy 99, pp. 689-721.
Enzler, Jared, Lewis Johnson and John Paulus. 1976. "Some Problems of Money Demand." Brookings Papers on Economic Activity
1, pp. 261-280.
Estrella, Arturo. 1987. "Domestic Banks and Their Competitors in
the Prime Commercial Loan Market." In Commercial Bank Profitability. Federal Rc3erve Bank of New York.
James, Christopher. 1987. "Some Evidence on the Uniqueness of
Bank Loans." Journal of Financial Econorrzics 19, pp. 217-235.
Judd, John P. 1979. "Competition Between the Commercial Paper
Market and Commercial Banks." Federal Reserve Bank of San
Francisco Economic -L'?evielv (Winter) pp. 39~53.
Laderman, Elizabeth S., and Randall 1. Pozdena. 1991. "Interstate
Banking and Competition: Evidence from the Behavior of Stock
Returns." Federal Reserve Bank of San Francisco Economic
Review (Spring) pp. 32-47.
McCauley, Robert N., and Rama Seth. 1992. "Foreign Bank Credit
to U.S. Corporations: The Implications of Offshore Loans."
Federal Reserve Bank of New York Quarterly Review (Spring)
pp.52-65.
Merton, Robert C. 1977. "An Analytic Derivation of the Cost of
Deposit Insurance and Loan Guarantees." Journal ofBanking and
Finance 1, pp. 3-11.
Pennacchi, George G. 1987. "A Reexamination of the Over- (or
Under-) Pricing of Deposit Insurance." Journal ofMoney, Credit,
and Banking 19, pp. 340-360.
Fama, Eugene F. 1985. "What's Different about Banks?" Journal of
Monetary Economics 15, pp. 29-39.
Phillips, P. C. B., and S. Ouliaris. 1990. "Asymptotic Properties of
Residual Based Tests for Cointegration." Econometrica 58, pp.
165-193.
Friedman, Benjamin M., and Kenneth N. Kuttner. 1991. "Why
Does the Paper-Bill Spread Predict Real Economic Activity?"
Working Paper Number 91-16. Federal Reserve Bank of Chicago.
Post, Mitchell A. 1992. "The Evolution of the U.S. Commercial
Paper Market Since 1980." Federal Reserve Bulletin 78, pp.
879-881.
Fuerbringer, Jonathan. 1991. "Commercial Paper Has Troubles
Too." New York Times (February 10), p. F4.
What Caused the 1990-1991 Recession?
Carl E. Walsh
Visiting Scholar, Federal Reserve Bank of San Francisco
and Professor of Economics, University of California,
Santa Cruz. I would like to thank, without implicating, Jim
Wilcox, Menzie Chinn, Judy Newman, TIm Cogley, Brian
Cromwell, Adrian Throop, and participants in the Brown
Bag Workshop at D.C. Santa Cruz for helpful comments.
This article decomposes u.s. GDP into components associated with major macroeconomic disturbances in order to
identify the likely causes ofthe 1990 recession. Four types
of disturbances-aggregate supply, aggregate spending,
money demand and money supply-are identified in the
empirical analysis. The results suggest the general slowing
of the economy relative to trend prior to the actual downturn was due to restrictive monetary policy. Aggregate
spending factors turned contractionary in mid-1990, however, and accountedfor most of the subsequent decline in
GDP during the rest of1990.
July 1990 marked the end of the longest peacetime expansion in the history of the U.S. economy. Real GDP grew at
an average annual rate of 3.3 percent from the fourth
quarter of 1982, the end of the previous recession, until the
third quarter of 1990. Unlike the two recessions the U.S.
suffered in the early 1980s, which were associated with
policies designed to bring inflation down from double digit
levels, the causes of the 1990-1991 recession have been less
apparent. Pessimistic consumers, the debt accumulations
of the 1980s, the jump in oil prices after Iraq invaded
Kuwait, a credit crunch induced by overzealous banking
regulators, and attempts by the Federal Reserve to lower
the rate of inflation all have been cited as causes of the
recession.
When economists discuss the sources of economic fluctuations within the context of their theoretical models of
the macroeconomy, they normally do so in terms of a small
number of fundamental disturbances. The structure of the
economy then leads these disturbances to be propagated
throughout the economy and over time in ways that generate the behavior typically associated with a business cycle.
The assumed nature of both the initiating shocks and the
propagation mechanism varies among different schools
of macroeconomic thought. For real business cycle proponents, disturbances to the economy's productive capacity,
usually referred to as technology shocks or, more generally,
as aggregate supply shocks, are the initiating factor, while
the attempts by households and firms to respond optimally
to these supply shocks result in the propagation over time
of the initial shock's impact on output, consumption, and
investment. 1
Other economists emphasize a wider range of possible
initiating shocks, including factors originating in the demand side of the economy (consumption, investment, government spending and taxation, net exports) and financial
factors such as monetary policy shocks or shifts in the demand for financial assets. These disturbances affect the
economy over time in ways that depend importantly on
the adjustment of expectations, wages, and prices.
If these views of the economy are useful in understanding the behavior of the macroeconomy, then it should be
1. For a survey of the real business cycle approach, see McCallum
(1989).
34
FRBSF ECONOMIC REVIEW
1993,
NUMBER
2
possible to identify the actual disturbances responsible for
observed fluctuations in terms of the small number of
shocks typically cited by economists in their discussions
of economic activity. That is, one can ask how important
aggregate supply, aggregate demand, and financial market
disturbances were in causing a recession. In turn, such an
identification may be useful both in deciding whether those
factors emphasized by a particular theory have in fact been
important, and, since the appropriate policy response may
differ depending on the source of fluctuations, in judging
how well policy has been implemented.
This paper decomposes output into components due to
various macroeconomic disturbances in order to identify
the factors that are most likely to have caused the 1990
downturn. To do so, the paper focuses on the evidence
obtained by estimating a structural vector autoregression.
This approach is similar to that adopted by Blanchard and
Watson (1986) and Gall (1992) and represents a starting
point for understanding the causes of the recession. By
identifying the general nature of the disturbance, or disturbances, responsible for the downturn, the paper represents a starting point, leaving for future research a more
detailed analysis of the determinants of these disturbances.
The empirical analysis suggests that the economy was
growing relative to its underlying trend through the middle
of the 1980s. Inflation also was rising during this period.
As measured by the Consumer Price Index, the rate of
inflation rose from 1.2 percent in 1986 to 4.4 percent in
1987 and remained at that level through 1989. 2 In response
to signs that inflation was beginning to revive, monetary
policy began to shift toward a more contractionary stance
in 1986. The Federal Reserve was motivated during this
period by a desire to move the economy towards zero inflation,as many economists have argued that zero inflation
will contribute to higher average real economic growth. 3
Restrictive monetary policy is estimated to have had a
significant role in slowing real economic growth relative to
trend in the period from 1986 to 1989. Beginning in 1989,
however, aggregate spending factors turned sharply downward. It is these factors that pushed the slowly growing
economy into recession.
Since the end of the recession in March 1991, the
recovery has been very slow, and many factors have been
identified as responsible for the weakness of the current
expansion. Such factors are not the focus of this paper, nor
are the factors that were at work once the recession started.
Instead, I focus exclusively on the developments leading to
the downturn in the middle of 1990.
2. Inflation averaged 4.4 percent in 1988 and 4.6 percent in 1989. It then
rose to 6.1 percent in 1990 before dropping to 3.1 in 1991.
3. For a discussion of the benefits of zero inflation, see Laidler (1990).
In order to understand the possible causes of the recession, this paper will employ a simple model that is used by
most intermediate level textbooks in macroeconomicsthe IS-LM model combined with an aggregate supply (AS)
function. This framework is reviewed in Section 1. An explanation of the approach adopted to implement the framework empirically is contained in Section II. Section III
discusses the implications of the estimated model to see
how well it conforms to the standard conclusions from the
IS-LM-AS framework. Section IV then uses the model to
obtain a decomposition of GDP that attributes movements
in GDP to underlying aggregate supply, IS, money demand, or money supply disturbances. This decomposition
leads to a further examination of the role of monetary
policy in Section V. Conclusions appear in Section VI.
I. A MACRO FRAMEWORK
Many economists organize their thinking about the macro
economy by using some variant of a simple framework that
links real and financial developments to a small number of
basic economic disturbances. The most common of these
frameworks is the IS-LM model of aggregate demand,
combined with an aggregate supply function. The resulting aggregate demand-aggregate supply model (AD-AS)
forms the core of most intermediate level textbooks
in macroeconomics. 4 This model attributes movements in
GDP to disturbances originating in either the factors affecting aggregate demand or aggregate supply, and within
aggregate demand, to either IS shifts (government fiscal
policy, consumption, investment, net exports), money
demand shocks, or money supply disturbances. Aggregate
supply shocks arise from disturbances such as technology
shocks or oil price changes that influence the economy's
supply of output. The purpose of this section is to outline a
simple AD-AS model that can be used to assess the role of
these various shocks on GDP during the period leading to
the downturn in mid-1990. 5
The building blocks of the basic IS-LM-AS model are:
1. An IS relationship showing the real demand for domestically produced output for given levels of interest rates
and prices
2. A monetary sector specifying the demand for money and
its supply (the LM relationship)
4. For example, Abel and Bernanke (1992), Dornbusch and Fischer
(1990), Gordon (1992), Hall and Taylor (1992), and Mankiw (1992) all
make use of an IS-LM plus aggregate supply framework.
5. For an empirical analysis of postwar U.S. economic activity before
1988 using an IS-LM-AS framework, see Gall (1992).
WALSH/WHAT CAUSED THE
3. An aggregate supply function showing the output level
consistent with the economy's capital stock and labor
market equilibrium.
These components of the IS-LM-AS model serve to explain the determination of real output, prices, and interest
rates. The framework is also used to predict the general
effects that various economic disturbances would have on
these macroeconomic va..'iables. For example, since money
wages appear to adjust relatively slowly and sluggishly,
increased demand for output, caused by a shock such as a
rise in government purchases, will raise domestic production, increase employment, and push up the level of
interest rates. Over time, wages and prices will rise,
reducing the level of output firms find it profitable to
produce, and production will return to its initial level. A
positive shock to the supply of money (or a shock that
lowers the demand for money) will act to lower interest
rates in order to maintain equilibrium in the money market.
Lower interest rates help to stimulate investment spending,
producing a rise in aggregate demand and output in the
short run. As prices then rise, the real supply of money is
reduced to its initial level, reversing the temporary movements in interest rates and output. Finally, a positive shock
to aggregate supply, such as an unanticipated decline in oil
prices, raises the level of output firms wish to produce.
Output expands and interest rates must fall to stimulate a
corresponding rise in aggregate demand. 6
The exact pattern of responses exhibited by the economy
as a result of economic disturbances will be determined by
the degree of flexibility in money wages and prices, the
extent to which disturbances are anticipated, and the role
played by expectations of both inflation and the policy
responses induced by economic fluctuations.
The next two sections describe the empirical approach
used to obtain estimates of the four basic disturbances and
their contributions to GDP movements. These sections are
somewhat more technical than the rest of the paper and
could be skipped by readers who wish to proceed directly
to the discussion in Section IV of the role of the various
disturbances.
n. THE EMPIRICAL FRAMEWORK
It is convenient to represent the empirical framework by a
four-equation system, consisting of an aggregate supply
equation, an IS equation, a money demand function, and a
money supply function (AS, IS, MD, and MS equations),
6. Increases in the price of imported oil also act as a tax on domestic
consumers, thereby reducing aggregate demand. The discussion in the
text presumes the supply effect dominates.
1990-1991
RECESSION?
35
that determines equilibrium values of real output (y), a
nominal interest rate (i), real money balances (m - p) and
the nominal supply of money (m). In its most general form,
we could write the model as
Adzt = B(L)dzt _
l
+
Et ,
where dz' = (dy, di, dm-dp, dm) is the vector of
endogenous variables, assumed to require first differencing
to induce stationarity,? A is a 4 x 4 matrix, B( ) is a 4 x 4
matrix polynomial in the lag operator L, and E is a 4 x 1
vector of the unobserved structural disturbances, E' = (E as
E is Emd Ems).
It is assumed that the elements of E are mutually uncorrelated and serially independent with diagonal variancecovariance matrix ~E" These represent the fundamental
disturbances impinging on the macroeconomy. Insight into
the cause, or causes, of the 1990-1991 recession can be
gained by obtaining an estimate of E and the contributions
of its four elements to movements in GDP leading up to the
onset of the recession.
While consistent estimates of A - lE can be obtained
from OLS regressions of J).Zt on lagged values of itself, 8 the
estimation of A requires the imposition of identifying
restrictions. A variety of means have been employed to
identify "structural VARs" (Bernanke 1986, Blanchard
and Watson 1986, Sims 1986, Walsh 1987, Shapiro and
Watson 1988, Blanchard 1989, Blanchard and Quah 1989,
Judd and Trehan 1989, King, Plosser, Stock and Watson
1991, Hartley and Walsh 1992, Hutchison and Walsh 1992,
Gall 1992, Moreno 1992). These generally take the form
either of zero restrictions on the A matrix or restrictions on
the long-run effects of elements of E on elements of z.
Zero restrictions imposed on elements of A directly
restrict the channels through which shocks can contemporaneously affect the macro variables in the system. For
example, in Walsh (1987), the aggregate supply relationship was taken to contain only output and prices. Therefore, any direct shock to interest rates was assumed to
affect aggregate output only by first affecting prices (relative to expectations). Restrictions on contemporaneous
interactions are, however, controversial. When expectations play an important role and agents use all relevant
information to form expectations, for instance, zero restrictions are difficult to justify.
Recent attempts to identify structural disturbances have
focused on the long-run effects of various disturbances and
the ways in which economic theory might imply restric-
7. The results of unit root tests, reported below, are consistent with this
assumption.
8. That is, by estimating ~z,=A -IB(L)Azt _ 1 +A -IE.
36
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
tions on these effects. For example, economists who employ a wide range of approaches generally agree that the
long-run effects of purely nominal disturbances fall entirely on prices and not on real magnitudes like the level of
output. Restrictions of this sort have been used by Shapiro
and Watson (1988), Blanchard (1989), King, Plosser, Stock
and Watson (1991), Hutchison and Walsh (1992), Gali
(1992), and Moreno (1992). Since similar long-run restrictions are implied by a variety of models, they have generally been viewed as less controversial than restrictions on
the contemporaneous interactions.
Four types of restrictions, all long-run in nature, are used
in this paper to identify the structural disturbances and
their impact on the variables in Z.9
Type 1. The long-run effect of IS, money demand, and
money supply shocks on the level of real GDP is
zero (3 restrictions)
long run as a result· of monetary disturbances. 10 If so, then
money demand and money supply shocks will also have no
long-run effect on the nominal rate of interest. 11 The final
restriction reflects the assumption that changes in the level
of the money supply ultimately produce proportionate
changes in the price level. This implies that real money
balances will not be affected in the long run by shocks that
affect only the level of the nominal supply of money. This
restriction is also consistent with conventional money
demand equations; if real money demand depends on
output and interest rates, neither one of which is affected in
the long run by shifts in the level of the money supply, then
real money balances also must be independent of money
supply shocks in the long run.
Incorporating these restrictions implies the following
system of equations which can be estimated by 2SLS as
discussed in Shapiro and Watson (1988):IZ
Type 2. The long-run effect of money demand shocks on
the level of nominal interest rates is zero
(l restriction)
Type 3. The long-run effect of money supply shocks on the
level of nominal interest rates is zero (1 restriction)
N~I
Type 4. The long-run effect of the level of money supply
shocks on the level of real money balances is zero
(l restriction)
The first category of restrictions (no long-run effect on
output of IS, money demand, or money supply disturbances) has been used previously by others in order to
distinguish between aggregate supply shocks, which potentially do have long-run output effects, and aggregate
demand shocks, which do not (for example, Blanchard and
Watson 1986, Blanchard 1989, Blanchard and Quah 1989,
Judd and Trehan 1989, Hutchison and Walsh 1992, Gali
1992, and Moreno 1992).
The next three types of restrictions are based on the
long-run dichotomy between the real and financial sectors
implied by most macroeconomic models. This dichotomy
implies that the real interest rate, the nominal rate corrected
for the expected rate of inflation, should be independent of
money demand and money supply disturbances in the long
run. If monetary disturbances, whether originating on the
demand or the supply side of the money market, do not
permanently alter the rate of growth of the money supply,
so that the rate of inflation is stationary, both real interest
rates and the rate of inflation should be unaltered in the
9. After an earlier draft of this paper was written, I read Keating (1992)
in which a VAR identified only through long-run restrictions, as is done
here, is reported. Keating's restrictions are the same as those used here.
+
Io
8 li d Zmt _
i
N
di t =
Io
+
N
(XZidYt-i
+
I1
Etas
~Zidit-i
N-I
+ Io
"YZidZ(m-p)t_i
N-I
+ Io
8ZidZmt_i
+ E/
S
10. Dickey-Fuller statistics reported in Section III are consistent with the
assumption that the inflation rate and the growth rate of money are
stationary .processes.
11. Tax effects that arise when nominal interest income, and not real
interest income, are taxed. might lead permanent changes in money
growL~ to have long-run effects on real interest rates by alteringthe rate
of inflation. As noted in the previous footnote, however, both money
growth and inflation are consistent with the assumption that they are not
subject to permanent shifts. Even in the presence of tax effects, there is
no reason to expect permanent changes in the level ofthe nominal money
supply to cause long-run changes in either real or nominal interest rates.
12. A constant also wasincluded in each equation for the purposes of
empirical estimation.
WALSH/WHAT CAUSED THE
N
a(m-p\ =
!.o <x3i aYt-i
N
+
!.0 133iait-i
N
+
!.1 'Y3i a (m-p)t-i
N
amt =
N
!.o <x4i aYt-i + I0
134iait-i
N
+
!.o 'Y4i a (m-p)t-i.
+
!.1 54i amt - i + etmS
N
The zero long-run impacts of IS, money demand, and
money supply shocks on real output are imposed by
constraining the sum of the coefficients on the current and
N lagged values of ai, (m - p) and 11m in the equation
for ay to be zero. This can be done directly by entering
these variables in second difference form (that is, 2i) and
includingonlyN -1 lagged terms. Since contemporaneous
values appear on the right hand side of the output equation,
the equation is estimated by 2SLS. As instruments, N lags
of the first differences of Y, i, m - p, and m were used.
In the equation for i, the zero long-run effect of money
demand and money supply shocks on the level of the nominal interest rate is imposed by including N - 1 lags of the
second differences of m - P and m. In addition to the instruments used in estimating the equation for ay, the
estimated residual for the output equation is used, since eas
and eis are assumed to be orthogonal.
Because the level of m is assumed to have no long-run
impact on the level of m - p, the money supply is entered in
second difference form with a lag length of N - 1 in the
equation for a (m - p). This is the only restriction imposed
on this equation. The estimated residual from the interest
rate equation is added to the set of instrumental variables to
estimate this equation. Finally, the equation for am is
unconstrained, and the residuals from the previous three
equations are used as instrumental variables, in addition to
N lags of the first differences of all the variables.
Once estimated, this system of equations can be used to
determine the contribution of the four fundamental shocks
to the movement of GDP during 1990. This will serve to
indicate the general source of the contractionary forces that
led to the downturn in 1990. However, alternativeidentifying restrictions could be used and might result in different
conclusions. The impulse response functions used to gen-
a
a
a
1990-1991
RECESSION?
37
erate the estimated contribution of each shock are themselves estimated relatively imprecisely. Any conclusions,
therefore, should be viewed as suggestive only.13
Galf (1992) estimates an IS-LM-AS model but uses
somewhat different identifying restrictions. He obtains
three restrictions by assuming the long-run output effects
of IS, money demand, and money supply disturbances are
equal to zero. These are the same restrictions listed as
Type 1 above and used in this paper. The remaining
restrictions Galf uses constrain the contemporaneous interactions of output, interest rates, prices, and money. Specifically, he assumes that neither money demand nor money
supply shocks have any contemporaneous effect on output.
For his final restriction, Galf considers three alternatives:
(a) prices do not enter the money supply rule contemporaneously; (b) GNP does not enter the money supply rule
contemporaneously; (c) price enters with coefficient one in
nominal money demand (money demand homogeneity).
If all three of these alternatives were imposed, the
system would be overidentified, and the overidentifying
restrictions could then be tested. Galf finds that assuming
(a), he rejects (e) but not (b). Assuming (b), he rejects (a)
but not (c), and assuming (c) he rejects (a) but not (b).
These conflicting results are difficult to interpret. Galf
reports the results he obtains under assumption (a), but
notes that generally similar results were obtained under the
alternatives.
Keating (1992) estimates a four-variable system involving output, an interest rate, real money balances, and the
money stock, using only long-run restrictions to achieve
identification. His restrictions are identical to the ones
employed here. The data used in the estimation differ
however. Keating used GNP, the GNP deflator, and Ml,
while GDP, the CPI, and M2 are used in this paper.
Ill.
ESTIMATION OF THE MODEL
This section discusses some further issues associated with
the estimation of the model. It also reports on the estimated
effects of the four disturbances on output, interest rates,
inflation, and money growth. These impulse responses
will be compared to the implications of the simple IS-LMAS framework that has motivated the model specification. These impulse response functions help castJight on
whether the empirical results accord with the theory. This
provides a check on the model; a close correspondence be13. As an alternative to the identifying restrictions listed above, real
federal defense expenditures were used to identify the model under the
assumption that these expenditures were correlated with IS shocks but
not with money demand shocks. The effects of using this alternative
specification were basically the same as those discussed in the text.
38
FRBSF ECONOMIC REVIEW
1993,
NUMBER
2
tween the theory and the estimated effects of the shocks
identified by the model should increase our confidence that
the restrictions used to identify the disturbances are appropriate. Differences between the results obtained in this
paper and those obtained by Gall and Keating also will be
discussed.
Estimation was carried out using quarterly data on the
logs of real GDP, M2, the CPI, and the level of the 3-month
Treasury bill rate over the period 1961.Q1 to 1991.Q2. All
data were taken from CITIBASE.14 A lag length offour was
used (N = 4), the same as used by Gali and Keating.
Implicit in the specification of the basic four-equation
system are two assumptions: (1) that the four variables in
the system (GDP, the 3-month T-bill rate, real M2, and
M2) are integrated of order 1, so that first differencing is
required in order to induce stationarity, and (2) that there
exist no cointegrating relationships linking the variables.
Both aspects of the specification are testable.
Table 1 reports the values of Phillips' zfJ. test statistic for
the unit root null. For all four variables in level form, the
test fails to reject the null hypothesis of a unit root. In each
case, however, first differencing induces stationarity in that
a unit root in the first difference can be rejected.
While the four variables in the system appear to be
integrated of order 1, there may exist linear combinations
of the variables that are stationary (integrated of order 0).
If so, the long-run behavior of the levels of the four
variables would be restricted, and these restrictions should
be incorporated into the estimated model. 15 Table 2 reports
the results of employing the multivariate test for cointegration developed by Johansen (1988). Johansen's trace test
and maximum eigenvalue test give conflicting indications about the possible presence of cointegrating relations
among the four variables. The trace test fails to reject the
null that the number of cointegrating vectors is less than
or equal to 1, taking on a value of 23.2 as compared to the
95 percent critical value of 35.07. The test statistic for
the null that the number of cointegrating vectors equals
zero takes the value 51.56 which is not significant at the
5 percent level. In contrast, the maximum eigenvalue
statistic for the null of zero cointegrating vectors against
the alternative of 1 is 28.36 which just exceeds the
95 percent critical value of 28.17. While the evidence
indicates that the system contains no more than one cointegrating vector, the results do not point unambiguously to
14. In the notation of CITIBASE, the basic variables used were GDPQ,
FM2, PUNEW, and FYGM3.
15. See Engle and Granger (1987), King, P1osser, Stock, and Watson
(1991), Johansen and Juselius (1990). In the presence of cointegrating
relationships, short- and long-run dynamics can be modeled by a vector
errOr correction (VEe) model.
TABLE 1
UNIT ROOT TESTS: PHILLIPS' ZIJ..
LEVELS
FIRST DIFFERENCES
GDP
-1.76
-8.90
3MTB
-2.40
-9.56
CPI
-0.80
-3.89
M2
-0.41
-5.20
M2--CPI
-1.98
-5.39
NOTE: GDP, M2, and ePI are in log form.
TABLE 2
COINTEGRATION TESTS
foUR-VARIABLE SYSTEM:
H*2
TRACE
GDP, 3MTB, CPl, M2
TRACE
AMAX
AMAX
0.95
0.95
r,,;;:3
4.90
9.09
4.90
9.09
r,,;;:2
12.07
20.17
7.17
15.75
r ,,;;: 1
23.20
35.07
11.13
21.89
r=O
51.56
53.35
28.36
28.17
oor 1. Consequently, I have proceeded under theassumption that the four variables are not cointegrated, leaving for
future work the estimation and analysis of an IS-LM-AS
model within the framework of an error correction model
that would incorporate the single cointegrating relationship
that might hold among these variables.
It should be noted, however, that other researchers have
found cointegrating relationships among the variables used
in this paper. Both Miller (1991) and Hafer and Jansen
(1992) report finding cointegrating relationships between
M2, prices, real output and interest rates. However, Miller's sample ends in 1987 and Hafer and Jansen's ends in
1988, and there is evidence of an apparent downward shift
in M2 demand beginning in 1990 (see Duca 1992 and
Feinman and Porter 1992). This may imply these variables
are no longer cointegrated. Since data up to the second
quarter or 1991 are used in this paper, the conflicting
evidence on cointegration may reflect the different sample
periods used in the various studies. Because cointegration
captures long-run relationships among time series variables, and long-run restrictions are employed to identify
the model in this paper, different assumptions about the
presence or absence of cointegrating relationships may
WALSH/WHAT CAUSED THE
influence the model estimates. The outcome of cointegration tests, however, has no necessary implications for the
long-run identifying restrictions, since cointegration is a
property of the stochastic disturbances (the E' s) while the
identifying restrictions are restrictions on the coefficients
of the model.
The objective is to obtain estimates of the disturbance
terms that can be interpreted within the framework of
the AD-AS model. For this interpretation to be valid, the
estimated effects of each type of disturbance should agree
with the basic implications of the theoretical framework.
The estimated model can be used to calculate the path of
output, prices, and interest rates in response to each of the
four underlying disturbances. Since the AD-AS framework
predicts the general shapes of these response functions, the
estimated responses can be used to see whether the data are
broadly consistent with the basic framework and the identifying assumptions made in the estimation process. For
example, a positive money supply shock is predicted to
lower nominal interest rates and raise real GDP in the short
run. Over time,real GDP should return to its initial path,
as should nominal interest rates, if the growth rate of
money is stationary. If the impact of the money supply
shock identified by the estimation process does not have
these characteristics, it would suggest that the shock has
not been correctly identified.
In addition to comparing the estimated impulse response functions to the predictions of the AD-AS framework, the findings are also related to the IS-LM-AS model
of Galf (1992) and to therecent paper by Keating (1992)
which used the same long-run restrictions as are employed
here. 16 With the exception of money demand shocks,the
results are in basic agreement with the implications of
the simple AD-AS framework. This provides some support
for the identifying restrictions used to obtain estimates of
the underlying disturbances .
Figure 1 shows the estimated responses to a positive
aggregate supply shock together with one standard deviation bands. Responses are shown out to 12 quarters; the
standard errors tend to become very large quickly and are
shown only for the first six quarters. The point estimates
indicate aggregate output is permanently increased by a
positive supply shock. Since equilibrium requires that
aggregate demand also rise permanently, the rate of interest falls. While inflation initially drops, money growth
increases, accormnodating the rise in output. The effects
on money growth and inflation, however, are temporary, so
16. Both Galfand Keating use GNP andMl in contrast to the use ofGDP
and M2in this paper. Some of the differences may therefore be due to
variable definition as well as to identif)dng restrictions.
1990-1991
RECESSION?
39
the decline in the nominal rate of interest implies a fall in
the real rate. These estimated responses are consistent with
a textbook model of AD-AS (for example, Hall and Taylor
1992) and look similar to the predicted capital accumulation path in a neoclassical growth model.
The estimated effects of a positive aggregate demand
shock are shown in Figure 2. Output peaks after five
quarters, and then declines gradually until it returns to its
initial level. Inflation is increased, but IS shocks have no
permanent impact on either money growth or inflation.
The permanent increase in the nominal interest rate, therefore, represents a rise in real rates. The rise in real rates is
needed to crowd out expenditures in order to reduce
aggregate demand to its initial level.
The AD-AS model predicts that a positive money demand shock should, if the monetary authority fails to
accommodate it, raise nominal interest rates temporarily
and contract aggregate demand. As Figure 3 shows, a
positive money demand shock does initially raise the
nominal interest rate slightly. The money supply also rises,
reflecting the fact that the Fed has partially accommodated
money demand shocks. However, the money demand shock
is still estimated to reduce real output, despite the accommodative policy response. It should be noted, however, that
the standard errors around the estimated money demand
effects are very large and none of the effects except the
accommodative response of the money supply are statistically different from· zero.
Finally, Figure 4 shows the estimated responses to a
money supply shock. Output exhibits the familiar humpshaped pattern associated with money shocks (King 1991),
and nominal interest rates initially decline. The impact of
the shock on the rate of growth of money is temporary, so
the impact on inflation is also.
The impulse response functions obtained from the estimated system accord well with the predictions of the basic
IS-LM-AS framework. They also are generally consistent
with the findings of Galf (1992) and Keating (1992),
although some of the specific estimated responses differ.
Gall's basic set of identifying restrictions differ from those
used in this paper. He assumes, as I do, that IS, MD, and
MS shocks have no long-run effects on real output. He then
assumes that neither money demand nor money supply
shocks have contemporaneous effects on real output. In
contrast, I allow both money market shocks to affect GDP
contemporaneously. Finally, Galf assumes that the money
supply does not respond contemporaneously to prices. As
discussed in the text, I impose the restrictions that money
supply and money demand shocks have no long-run impact
on the level of nominal interest rates and that money supply
shocks have no long-run impact on the level of real money
40
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
FIGURE 1
FIGURE 2
RESPONSES TO AN AS SHOCK
RESPONSES TO AN IS SHOCK
GDP
GDP
Percent
Percent
20
1. 5
0
1.
1
1
1
1.0
0.5
..
" """
,,"'"''''''
".,
o.ot-~-"--=",=---------
0.5
-0.5
INTEREST RATE
INTEREST RATE
Percent
Percent
2.0
1.0.
0.5
1.5
...0.0 1---.""'".",..,
........""'..,_.,
-------
,.,.,
,
-0.5
.... "",,,,
1.0
""" ' ,
-1.0
.,....,',....~"'"
,
..•...,....•
.
Interest Rate
,.,
Interest Rate
0.5
·1.5
INFLATION
INFLATION
Percent
Percent
1.0
2.0
0.5
0.0t--~""···""·.."="'--·,,·"_···- - - - - - - ·0.5
.......................
1.0
Inflation
·1.0
·1.5
·2.0
.......••.......".,.
-
1.5
,
..,..
,'"
0.5
0.0 -f-----+J?r,;c---'----------
.
-0.5
-2.5
-3.0 +----,--------,---------,--~-~10C--.----,12
M2GROWTH
M2GROWTH
Percent
Percent
2.0
1.0
1.5
1.0
f:'"·············".
...... ".",
,
0.5
0.5
0.0 •
o.ot--H----"c-------'l.=------·0.5
M2Growth
·0.5
,
·1.0
.
e--~-~-~-~--~~10--12
WALSH/WHAT CAUSED THE
1990-1991 RECESSION?
FIGURE 3
FIGURE 4
RESPONSES TO AN MD SHOCK
RESPONSES TO AN MS SHOCK
GDP
GDP
Percent
Percent
2.0
"]
1.5
0.5
1
1.0
GOP
·0.5
GDP
0.5
0.0
0.0
................... " ................."
·0.5
·1.0
10
12
.1.0
INTEREST RATE
INTEREST RATE
Percent
Percent
1.0
1.0
0.5
0.5
",
10
12
10
12
10
12
0.0
.........................................
Interest Rate
. ......../ ..........
·1.0
10
12
-1.0
INFLATION
INFLATION
Percent
Percent
1.0
2.0
. . . '. . . . . . . . . .
,f
l
...........
,"',
·0,5
·0.5
1.5
0.0.
1.0
Inflation
·0.5
12
..................... ,.......
................"....................
0.0
0.5
10
..............""" ..,.",""",.,...
... ...
,., ,
0.5
.................................,.
Inflation
""""",',
·1.0
0.0
·1.5
·0.5
·2.0
10
12
.....•..•..-......."" ...,...,.,.,..............
·1.0
M2 GROWTH
M2 GROWTH
Percent
Percent
2.0
2.0
1.5
1,5
1.0
0.5
0.0
1.0
.....................................
\\\
M2Growth
0.0
M2Growth
-0.5
............................
.....•....................
-1.0
·0.5
·1.0
.....,.
0.5
·1.5
10
12
·2.0
41
42
FRBSF ECONOMIC REVIEW 1993,
NUMBER
2
balances. These last three restrictions seem better motivated by economic theory than do Gall's.
One difference that the alternative restrictions make is
evident in the estimated impact of an IS shock. Galf finds
that a positive IS shock permanently raises nominal money
growth and inflation, with the inflation rate rising between
two and three times the increase in the growth rate of the
money supply. Under my restrictions, the long-run effect
on the rate of money growth must be the same as the longrun effect on the rate of inflation; in the long run, inflation
equals the rate of money growth. I estimate IS shocks to
have no long-run effect on the rate of growth ofM2, so such
shocks also have no long-run effect on the rate of inflation.
Galf also finds that a positive IS shock permanently lowers
the real rate of interest. The real rate rises in the model I
estimate.
IV:
DECOMPOSING
GDP
The role of the four shocks identified by estimating the
model is most informatively displayedhy expressing the actuai movement in GDP as the sum of the individual
contributions of each of the four disturbances. I7 That is,
GOP in a specific quarter can be written as the sum of the
contribution of current and past aggregate supply shocks,
current and past IS-shocks, current and past money demand shocks, and current and past money supply shocks
plus any deterministic trend. Such "historical decompositions" provide estimates of the cumulative effect of the
various shocks on GOP.
Before focusing specifically on the recent recession, it is
useful to examine past recessionary experiences to determine if the model succeeds in identifying as their causes
those factors that are generally accepted to have played
important roles in previous downturns. Figure 5 presents
the historical decomposition of GOP into components
attributed to each of the four orthogonal shocks. In the
upper panel, the solid line is actual GOP, while the dashed
line is the estimated contribution of aggregate supply factors and the deterministic drift in GOP. These are the
factors responsible for the stochastic trend in GOP. The
lower panel shows the estimated contribution oflS, money
demand, and money supply factors to the cyclical component of GOP. The sum of these three components equals
the difference between actual GOP and the aggregate
supply component shown in the upper panel.
17. Since the disturbances are, by assumption, orthogonal, the sum of
the individual contributions of the four shocks exactly equals the
nondeterministic component of GDP.
FIGURE 5
HISTORICAL DECOMPOSITION OF
GDP
Log
8.6
8.5
8.4
8.3
8.2
8.1
8.0
7.9
7.8
7.7
65
71
68
74
77
80
83
86
89 91
83
86
89 91
Log
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
65
68
71
74
77
80
The upper panel shows that aggregate supply disturbances exerted contractionary effects on the economy in
1973-1974 and in 1979-1980. These dates correspond to
the oil price increases, indicating that the supply shock
identified by the model is correctly picking up these
disturbances. Money supply disturbances are estimated to
have had major contractionary effects leading into the 1969
recession and during the two recessions in the early 1980s.
This latter period is associated with the Volcker deflation,
and the model successfully identifies monetary policy as
an important cause. of these recessions. Money supply
factors are estimated to have had a major expansionary
impact from 1974 to 1977,again agreeing with most
WALSH/WHAT CAUSED THE 1990-1991 RECESSION?
accounts that attribute the run-up in inflation during this
period to excessively expansionary monetary policy.
While money demand factors often show large swings,
these are not as clearly associated with specific business
cycle fluctuations; IS shocks, however, are estimated to
have contributed to the 1974 recession and the 1981-1982
recession. The Reagan fiscal expansion of the early 1980s
fails to show up in any major way. These findings contrast
somewhat with those of Gall who also finds a fiscal
contraction contributing to the 1981-1982 downturn but
finds a strong fiscal expansion occurring from 1982 to
1985.
According to the first panel of Figure 5, the expansion
that began in 1982.Q4 and ended in 1990.Q3 started below
GDP's estimated aggregate supply-trend component, but
moved above this component in early 1984. After growing
more slowly in 1986, GDP grew faster than its trend growth
rate during 1987 and 1988. It then slowed again relative to
its aggregate supply component during 1989 before the expansion ended in mid-1990. In evaluating the entire period
shown in Figure 5, it is worth noting that the model assumes aconstant average growth rate for the whole sample
period. IS
Confirming Gall's finding, money supply factors played
a key role in the early stages of the expansion. From the end
of 1982 until the first quarter of 1986, when output growth
temporarily slowed, almost half of the rise in GDP is
attributed to monetary expansion. Most of the remaining increase is attributed to aggregate supply factors. IS
shocks and MD shocks, in contrast, each had essentially no
netimpact during this period. Apparently the fiscal expansion in 1983 and 1984 associated with the Reagan tax cuts
and defense buildup was subsequently offset completely by
the dollar appreciation of the first half of the 1980s.
From 1987 through 1989, IS factors become less contractionary and actually tum expansionary in 1989.Ql.
This is almost completely offset by the contractionary shift
in the money supply component of GDP. Thus,the dollar
depreciation of this period appears to show up in the IS
series,p~tt~~Federal Reserve's policy of gradually reducingtn:~;.rtti~~tinflation to zero stabilized real economic
activity in the face of what otherwise would have been an
IS-driven expansion. This period seems to be consistent
with the Fed's desire at the time to engineer a smooth
landing, reducing the rate of inflation by slowing the
economy down without pushing it into a recession.
18. A dummy was included in the GDP growth equation to allow for a
shift in the trend growth rate in 1973. However, the coefficientoll the
dummy was statistically insignificant, so it· was· dropped from the
version of the model used to generate the results reported here.
43
The economy is estimated to have weakened significantly relative to its aggregate supply component eighteen
months before the official downturn in 1990.Q3. GDP
peaked relative to its aggregate supply component in
1988.Q4. I9 Approximately 27 percent of the decline in the
stochastic component of GDP from 1988.Q4 to 1990.Q2
was due to aggregate supply factors, while 56 percent was
due to money supply factors. The remaining 17 percent
was due to IS (12 percent) and MD (5 percent) factors. This
composition changed markedly once the recession started
during the second half of 1990. From 1990.Q2 to 1990.Q4,
over 90 percent of the decline in the stochastic component
of GDP is associated with the IS component. This is
consistent with the marked decline in consumer confidence
and consumption spending at the time of the Persian Gulf
crisis. Consumption, for example, declined at a 15 percent
annual rate during the fourth quarter of 1990, while private
investment spending dropped at a 35 percent annual rate in
this same quarter. 20 Net exports grew strongly in late 1990,
but not enough to offset declines in the other components
of aggregate spending.
The evidence in Figure 5 seems to suggest that the
positive contribution of money supply factors peaked in
late 1985 or early 1986. These factors acted to reduce the
level of GDP after late 1987. Growth was sustained mainly
due to a turnaround in IS factors, possibly associated with
the dollar depreciation that occurred during this period.
This is illustrated in Figure 6 which shows the actual path
ofGDP (the solid line) and two hypothetical paths (dashed
lines) assuming (1) no money supply effects after 1988.Q4
and (2) no IS effects after 1988.Q4. The line showing no
money supply effects suggests that the· economy would
have grown more strongly in 1989 than it actually did if
19. Romer (1992) argues that before 1927 NBER reference dates for U.S.
business cycles were based on detrended data; those after 1927 were
based on data in levels. Based on the earlier methods, the 1990 recession
would have started in 1988.
20. The cause of the sharp fall in consumption during the initial quarter
of the recession is probably attributable to the Gulf crisis. In August
1990, Iraq invaded Kuwait, and, over the next three months, the
Michigan Index of Consumer Sentiment (ICS) registered its biggest
three-month decline since its inception in 1956. And drops in ICS tend
to be associated with reductions in consumer spending, particularly on
durable goods (Throop 1991, 1992). Consumer purchases of durables
fell at just over a 15 percent annual rate during the fourth quarter
of 1990. Consumer sentiment is generally related to direct measures of
economic conditions, such as unemployment, interest rates, oil prices,
and inflation. In an error correction model of ICS, Throop (1992) finds a
significant negative coefficient on a dummy variable for the Gulf War,
indicating that the fall in consumer sentiment in late 1990 was not
directly related to current or recent economic conditions. The Gulf crisis
seems to have generated increased uncertainty on the part of households
and to have led directly to a reduction in consumer spending.
44
FRBSF ECONOMIC
REVIEW
1993,
NUMBER
2
FIGURE 6
HYPOTHETICAL
pact of monetary policy on the economy, then theexperience of the late 1980s may hold important lessons for the
ability of the Federal Reserve to reduce inflation gradually
without so weakening the economy that it is vulnerable to
recession. Credible policies designed to reduce inflation
are often thought to have little output cost. The contractionary impact of monetary policy in the late 1980s casts
doubt on this view, or on the credibility of the Federal
Reserve's policy of inflation reduction. 22
The implications of these findings might be quite different, however, if the money supply disturbances identified
by the model do not reflect monetary policy actions but
rather capture nonpolicy related banking sector factors.
Thus, the next section will examine some commonly employed indicators of monetary policy to determine whether
they tell a similar story. This will help to provide a check on
the robustness of the conclusions generated by the model.
GDP
Percent
30 [
25
20
15
10
5
V.
83
85
87
89
91
NOTE: 1982.Q4=O.
money supply factors had not turned more restrictive;
however, a recession still would have occurred. The line
showing no IS effect clearly indicates that the economy
would have suffered a short recession in 1989 if IS effects
had not been so expansionary. If both money supply and IS
factors had remained unchanged after 1988.Q4, the economy would have continued on a relatively flat path at least
through the middle of 1991, but no recession would have
occurred.
The historical decompositions indicate that, while money
supply shocks were pushing up GDP relative to trend
growth until 1987 , they had an increasingly contractionary
effect on economic activity leading up to the cyclical peak
as the Fed sought to prevent the economic expansion from
putting upward pressure on inflation. 21 In terms of the
timing of the peak in the stochastic component of GDP in
1988.Q4, the decline in the money supply contribution
seems responsible. While the contractionary impact of IS
factors in 1990 eventually would have generated an economic downturn, the role played by money supply factors
deserves a closer examination. If the model-based measure
of money supply disturbances actually reflects the im~
21. In reporting on its policies during 1989, the Fed stated that "Early
in the year, the economy still was strong, and inflation appeared to be on
the rise; to prevent the pressure on wages and prices from building, the
Federal Reserve extended the tightening of money market conditions
that had begun in early 1988." (Board of Governors 1989, p. 3.)
THE ROLE OF MONETARY POLICY
The previous section has suggested that money supply
factors from 1987 to 1989 may have contributed to the
slowing of the economy before the actual downturn. in
mid-1990. The money supply contribution to GDP is
estimated to have flattened in 1988 and then become more
contractionary during the first quarter of 1989. This raises
the question of whether monetary policy was responsible
for the contractionary shift. 23 In this section, several alternative indicators of monetary policy are examined to
determine whether they also are consistent with the view
that monetary policy became increasingly restrictive after
1988. The model-based measure is an estimate of the
exogenous component of money supply movements. In
contrast, these other indicators are endogenous variables
whose movements will reflect both policy and nonpolicy
factors. 24
In contrast to the model-generated measure shown in
Figures 5, the impact of monetary policy is more commonly measured by either a monetary aggregate, such as
M2, or an interest rate or interest rate spread. While the
importance of monetary aggregates, particularly M1,has
been downplayed in the policy process over the past ten
years, the Federal Reserve continues to establish target
zones for the M2 aggregate. M2's behavior is influenced by
22. For evidence that the Fed's inflation policy did not have credibility,
see Judd and Beebe (1993).
23. Given the lag between a change in monetary policy and its impact on
GDP (see Figure 4), the quotation in footnote 21 is consistent with the
downturn in MS in early 1989.
24. The Boschen and Mills index discussed below is an exception.
WALSH/WHAT CAUSED THE
factors other than Federal Reserve actions. For example,
the behavior of M2 in 1990 might reflect non-monetary
policy disturbances such as a possible credit crunch resulting from tighter bank supervision. Despite this, movements in M2 are often taken to indicate the stance of policy.
There was a marked slowdown in M2 growth in 1987.Q1.
From 1982.Q4 to 1986.Q4, M2 growth averaged 9 percent; from 1987 .Ql to 1990.Q2, it averaged 5.2 percent. In
Rotenberg, Driscoll, and Poterba (1991, Figure 1), their
currency equivalent monetary aggregate also shows a
slowdown in 1987. Such slowdowns in money growth
would be expected to affect nominal income growth with a
lag.· King (1991) estimates that the lag between changes in
M2 growth rates and M2 's peak effect on real economic
activity is approximately six to seven quarters, suggesting
that monetary policy was contributing to a slowdown in the
economy through late 1988 and 1989. This is consistent
with the evidence from the historical decomposition based
on the estimated AD-AS model.
In the standard IS-LM framework, changes in the money
supply act on real interest rates and the real economy by
affecting the real supply of money, the nominal supply
adjusted for the price level. Real M2 growth, like M2
growth itself, indicates a sharp tightening of monetary
policy in early 1987. The growth rate of real M2 fell from
7.5 percent in the fourth quarter ofl986 to ~ 0.2 percent in
the fourth quarter of 1987. From 1982.Q4 to 1986.Q4, the
four-quarter growth rate of real M2 averaged 5.7 percent; it
averaged only 1 percent from 1987.Ql to 1990.Q2. After
growing very rapidly through 1986, real M2 remained
roughly constant from 1987 through 1991. However, several authors attribute the slowdown in real M2 growth,
particularly after 1990, to a shift in M2 demand (Duca
1992, Feinman and Porter 1992). Duca finds that most of
the fall in M2 demand was the result of the closing of thrifts
by the Resolution Trust Corporation. If this is the case, the
failure of real M2 to grow during the period from 1987 to
1991 reflects a shift in money demand, not money supply or
monetary policy. The model does show a slight positive
effect of money demand shocks on output during 1990 and
1991 (see Figure 5), but it may be that the money supply
series is also picking up some of this money demand shift.
All three quantity indicators of monetary policy-the
model-based series, M2, and real M2-paint a similar
picture. They suggest monetary policy turned increasingly
restrictive in early 1987. The model-based estimate suggests the expansionary effect of monetary policy peaked in
early 1986, having a net negative impact on GDP beginning in late 1987. The Fed's own view is that its policy
became more restrictive only later, in March of 1988 at a
time when they felt the likelihood of higher inflation was
increasing (Board of Governors 1988). In the absence of
1990-1991
RECESSION?
45
offsetting developments, it is likely that a recession would
have occurred sometime in the period from late 1987 to
early 1990. Figure 5 suggests that the impact of monetary
policy during this period was offsetting aggregate spending (IS) factors.
In addition to quantity measures, interest rate movements are often used to gauge the stance of monetary policy, although these too are controversial as measures of
policy. Since the Federal Reserve has generally used operating procedures oriented toward interest rates, short-term
interest rate changes provide information about the actions
of the Fed. Recently, Bernanke and Blinder (1992) have
argued that the federal funds rate is' a good indicator of
monetary policy. The federal funds rate adjusts to equate
the demand for and supply of bank reserves, and Bernanke
and Blinder use monthly and weekly data to demonstrate
that the federal funds rate has been relatively insensitive to
fluctuations in reserve demand. This is consistent with the
view that movements in the funds rate reflect supply factors, including Federal Reserve policy actions. While the
evidence presented by Bernanke and Blinder deals with
the pre-October 1979 period, the funds rate also should
reflect mainly policy actions by the Fed under the borrowed
reserves operating procedure used during the past decade
(Walsh 1990). Restrictive monetary policy, by reducing the
supply of bank reserves, leads to a rise in the funds rate.
The sharp rise in the funds rate shown in Figure 7 prior to
the business cycle peaks in January 1980 and July 1981 is
consistent with the view that restrictive monetary policy
played a major role in the recessions of the early 1980s. The
funds rate did rise steadily beginning in 1986, moving from
6.21 percent in the third quarter of 1986 to a peak of9. 73 in
the second quarter of 1989. The funds rate, therefore,
indicates restrictive monetary policy continuing much
longer than was suggested by the growth rate of either M2
or real M2. Given the lags with which monetary actions are
normally thought to affect the real economy, the rise in the
funds rate is consistent with a monetary-induced slowdown
in 1990.
The funds rate is not an exogenous measure of monetary
policy, and its level is affected by such factors as the
prevailing expected rate of inflation. Variations in expected
inflation make interpreting the funds rate as an indicator of
monetary policy difficult. Since it is often thought that
short-run movements in long-term interest rates predominantly reflect variations in expected inflation, the funds
rate minus a long-term rate provides an alternative indicator of monetary policy (Laurent 1988, Goodfriend" 1990).
In Figure 7, FFBOND is the difference between the Fed
funds rate and the rate on lO-year constant maturity government securities. An increase in this series-that is, a rise
in the funds rate relative to the lO-year rate-would signal
46
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
FIGURE 7
FEDERAL FUNDS RATE AND
FFBOND
Percent
20
15
Federal Funds
Rate
10
5
-5
L..J----'--..J-l-
65
68
71
74
77
80
83
86
89 91
restrictive monetary policy. From the fourth quarter of
1987 to the third quarter of 1989, this series rose from
-2.21 percent to 0.98 percent. In describing this rise,
Bernanke and Blinder (1992, p. 17) state that "only two
sustained increases in FFBOND were not followed by
recessions. The first such episode, which was long and
gradual, ended with the 1966 credit crunch, which was
followed by a 'growth recession.' The second is the very
recent run-up which, as of this writing (September 1990),
has not led to a recession." We now know that the recession
had begun in July 1990.
. Indicators of monetary policy based either on monetary
aggregates or on interest rates are indirect measures, since
they are affected both by policy actions and by other
factors. Shifts in money demand, the impact of a credit
crunch, balance sheet restructuring and the S & L crisis are
just a few of the developments that make it difficult to rely
on only one indicator. In a recent study, Boschen and Mills
(1991) have constructed a measure of policy that is based
directly on their reading of the minutes of FOMC meetings. 25 They characterize policy as falling into five categories: contractionary, somewhat contractionary, neutral,
somewhat expansionary, and expansionary. Values of - 2,
-1, Oland 2 are assigned to these categories. The series
they construct is again consistent with the earlier evidence
25. Data through July 1991 were kindly supplied by John Boschen.
of restrictive monetary policy through most of 1987 and
1988. The index was equal to 1.0 (somewhat expansionary)
during all of 1986. It then fell to -1.0 by the third quarter
of 1987,rose to 1.0 in the fourth quarter of 1987 in response
to the stock market crash, then declined to a value of - 2.0
(contractionary) in the second quarter of 1989. Beginning
in the third quarter of 1989, monetary policy became progressively more expansionary according to the Boschen
and Mills index. This timing is consistent with the Fed's
own view. According to the Federal Reserve Board's 1989
Annual Report, "In June, the FOMC began a series of
steps-undertaken with care to avoid excessive inflationary stimulus-that trimmed 112 percentage points from
short-term interest rates by year-end" (p. 3). Given the lags
with which monetary policy affects the real economy, however, the Boschen-Mills series, like the other measures
examined, suggests that monetary policy was exerting a
contractionary effect on the U.S. economy from late 1986
or early 1987 until at least the middle of 1989.
Monetary policy clearly did not cause the 1990 downturn. Instead, monetary policy turned contractionary well
before the end of the expansion. Tne model-based historical decomposition shown in Figure 5 indicates that a
monetary-induced recession failed to occur in 1989 because it was offset by IS-originating factors. And it was the
downturn of these factors that pushed an economy already
slowed by restrictive monetary policy into recession in
1990.
VI.
SUMMARY AND CONCLUSIONS
An empirical model designed to represent a simple ISLM-AS framework was estimated in order to associate
movements in GDP with the four fundamental shocks emphasized by this framework. While the impulse response
functions generally matched the behavior implied by the
theoretical framework, thereby lending some support to
the method used to identitY the underlying shocks, the effects are not estimated with much precision. However,
the historical decompositions derived from the estimated
model did seem to capture those factors usually viewed as
important in previous recessions.
When the model was used to identitY the basic disturbances that might have caused the 1990 recession, three
points emerged from the analysis. First, while the timing of
the downturn in July 1990 was clearlyre1ated to the loss
of consumer and business confidence at the time of the
Gulf crisis, the economy had already significantly weakened, peaking relative to trend over a year earlier. Second,
the general weakness in the economy in the period leading
up to the actual cyclical peak was due to restrictive
monetary policy that served to offset expansionary IS
WALSH/WHAT CAUSED THE
factors in a way that kept the economy relatively flat. Such
a path seems consistent with the Federal Reserve's stated
goal at the time to bring inflation gradually-down closer to
zero. Third, IS factors turned (:lown in 1989.Q3, acting
to reduce the level of GDP beginning in 1990.Q1. These
IS factors accounted for most of the decline in GDP over
the rest of 1990. Thus, a more detailed examination of the
causes of the recession should begin by investigating
the reasons for the downward shift in the IS curve.
1990-1991
RECESSION?
47
REFERENCES
Abel, Andrew B., and Ben S. Bernanke. 1992. Macroeconomics.
Reading, MA: Addison-Wesley.
Bernanke, Ben. 1986. "Alternative Explanations of the Money-Income
Correlation." Carnegie-Rochester Conference Series on Public
Policy (Autumn) pp. 49-100.
_ _ _ _ , and Alan Blinder. 1992. "The Federal Funds Rate and
the Channels of Monetary Transmission." American Economic
Review 82 (September) pp. 901-921.
Blanchard, Olivier 1. 1989. "A Traditional Interpretation of Economic
Fluctuations." American Economic Review 79, pp. 1146-1164.
_ _ _ _ , and Danny Quah. 1989. "The Dynamic Effects of
Aggregate Demand and Supply Disturbances." American Economic Review 79, pp. 655-673.
_ _ _ _ _ , and Mark W, Watson. 1986. "Are Business Cycles All
Alike?" In The American Business Cycle: Continuity and Change,
ed. R.1. Gordon, pp.123-156. Chicago: University of Chicago
Press.
Board of Governors of the Federal Reserve System, 75th Annual Report,
1988.
Board of Governors of the Federal Reserve System, 76th AnnualReport,
1989.
Boschen, john E, and Leonard O. Mills. 1991. "The Effects of
Countercyclical Monetary Policy on Money and Interest Rates: An
Evaluation of Evidence from the FOMC Documents." Federal
Reserve Bank of Philadelphia Working Paper No. 91-20 (October).
Dornbusch, Rudiger, and Stanley Fischer. 1990. Macroeconomics. 5th
ed. New York: McGraw Hill.
Duca, John V. 1992. "The Case of the Missing M2," Federal Reserve
Bank of Dallas Economic Review (Q2) pp. 1-24.
Engle, Robert E., and C. w,1. Granger. 1987. "Cointegration and Error
Correction: Representation, Estimation and Testing." Econometrica 60, pp. 251-276.
Feinman, Joshua N., and Richard D. Porter. 1992. "The Continued
Weakness in M2." Finance and Economics Discussion Series No.
209. Board of Governors, Federal Reserve System (September).
Gall, Jordi. 1992. "How Well Does the IS-LM Model Fit Postwar U.S~
Data?" Quarterly Journal ojEconomics 107 (May) pp. 709-738.
Goodfriend, Marvin. 1990. "Interest Rates and the Conduct of Monetary Policy." Federal Reserve Bank of Richmond Working Paper
90-6 (August). Republished in Carnegie-Rochester Conference
Series 34 (Spring 1991) pp. 7-30.
Gordon, Robert. 1992. Macroeconomics. 6th ed. Boston: Little, Brown
and Company.
Hafer, R.W., and DennisW. Jansen. 1991. "The Demand for Money in
the United States: Evidence from Cointegration Tests." Journal oj
Money, Credit and Banking 23 (May) pp. 155-168.
Hall, Robert, and John B. Taylor. 1992. Macroeconomics. 3rd ed. New
York: W. W, Norton.
Hartley, Peter, and Carl E. Walsh. 1992. "A Generalized Methods of
Moments Approach to Estimating a 'Structural Vector Autoregression'." Journal ojMacroeconomics 14 (Spring) pp. 199-232.
48
FRBSF ECONOMIC REVIEW 1993, NUMBER 2
Hutchison, Michael M., and Carl E. Walsh. 1992. "Empirical Evidence on the Insulation Properties of Fixed and Flexible Exchange
Rates: The Japanese Experience." Journal of International Economics 32, pp. 241-263.
Johansen, Soren.1988. "Statistical Analysis of Cointegration Vectors."
Journal of Economic Dynamics and Control 12, pp. 231-254.
_ _ _ _ _ , and Katarina Juselius. 1990. "Maximum Likelihood
Estimation and Inference on Cointegration - With Applications to
the Demand for Money." Oxford Bulletin of Economics and
Statistics 51, pp. 169-210.
Judd, John P., and JackH. Beebe. 1993. "The Credibility of the Federal
Reserve's Disinflation Policy." Federal Reserve Bank of San Francisco Economic Review (forthcoming).
_ _ _ _ _ , and Bharat Trehan. 1989. "Unemployment Rate Dynamics: Aggregate Demand and Supply Interactions." Federal
Reserve Bank of San Francisco Economic Review, (Fall) pp. 20-37.
Keating, John W. 1992. "Structural Approaches to Vector Autoregressions." Federal Reserve Bank of St. Louis Review, 74 (Sept./Oct.)
pp.37-57.
King, Robert G. 1991. "Money and Business Cycles." Paper presented
at the Fall Academic Conference of the Federal Reserve Bank of
San Francisco.
_ _ _ _ _ , Charles 1. Plosser, James H. Stock, and Mark W.
Watson. 1991. "Stochastic Trends and Economics Fluctuations."
American Economic Review 81 (Sept.) pp. 819-840.
Laidler, David. 1990. "The Zero-Inflation Target: An Overview of the
Economic Issues." In Zero Inflation: The Goal of Price Stability,
ed R. G. Lipsey. Policy Study 8. C.D. Howe Institute.
Laurent, Robert D. 1988. "An Interest Rate-Based Indicator of Monetary Policy." Federal Reserve Bank of Chicago Economic Perspectives (Jan.lFeb.) pp. 3-14.
Mankiw, N. Gregory. 1992. Macroeconomics. NY: Worth Publishers.
McCallum, Bennett. 1989. "Real Business Cycle Models." In Modern
Business Cycle Theory, ed. Robert Barro. Cambridge: Harvard
University Press.
McNees, Stephen K. 1992. "The 1990-1991 Recession in Historical
Perspective." New England Economic Review (January/February)
pp.3-22.
Miller, Stephen M. 1991. "Monetary Dynamics: An Application of
Cointegration and Error-Correction Modeling." Journal ofMoney,
Credit, and Banking, 23 (May) pp. 139-154.
Moreno, Ramon. 1992. "Demand Shocks, Supply Shocks and Cyclical
Behavior: The Evidence from Japan." Unpublished paper. Federal
Reserve Bank of San Francisco.
Romer, Christina D. 1992. "Remeasuring Business Cycles." NBER
Working Paper No. 4150 (August).
Rotenberg, Julio 1., John C. Driscoll, and James M. Poterba. 1991.
"Money, Output and Prices: Guidance from a New Monetary
Aggregate," NBER Working Paper No. 3824 (August).
Shapiro, Matthew D., and Mark W. Watson. 1988. "Sources of
Business Cycle Fluctuations" In Macroeconomics Annual, ed.
Stanley Fischer. Cambridge: M.LT. Press.
Sims, Christopher A. 1986. "Are Forecasting Models Usable for Policy
Analysis?" Federal Reserve Bank of Minneapolis Quarterly Review (Winter) pp. 2-16.
Throop, Adrian. 1991. "Consumer Sentiment and the Economic Downtum." Federal Reserve Bank of San Francisco Weekly Letter
(March 1).
_ _ _ _ _ . 1992. "Consumer Sentiment: Its Causes and Effects."
Federal Reserve Bank of San Francisco Economic Review 1,
pp.35-59.
Walsh, Carl E. 1987. "Monetary Targeting and Inflation: 1976-1984."
Federal Reserve Bank of San Francisco Economic Review (Winter)
pp. 5-15.
_ _ _ _ _ . 1990. "Issues in the Choice of Monetary Policy Operating Procedures." In Monetary Policy for a Changing Financial
Environment, ed. W. Haraf and P. Cagan, pp. 8-37. Washington,
D.C.: AEI Press.
@FedFRASER