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S o m e Empirical E v i d e n c e o n the
Effects of M o n e t a r y Policy S h o c k s
o n E x c h a n g e Rates

Martin Eichenbaum and Charles Evans

Working Papers Series
Macroeconomic Issues
Research Department
Federal Reserve Bank of Chicago
December 1992 (WP-92-32)

FEDERAL RESERVE B A N K
OF CHICAGO

Some E m p iric a l E vidence on th e Effects of
M o n e ta ry P o licy Shocks on Exchange R ates
Martin Eichenbaum*
*
Northwestern University, N BER and
the Federal Reserve Bank of Chicago
Charles Evans*
Federal Reserve Bank of Chicago
December 12 1992

*We would like to thank Steve Strongin for many helpful conversations.
*The views expressed in this paper do not necessarily reflect the views of the Federal Reserve
Bank of Chicago or the Federal Reserve System.




1

Abstract
This paper presents new empirical evidence on the effects of monetary policy shocks on
U.S. exchange rates, both nominal and real. Three measures of monetary policy shocks are
considered: orthogonalized shocks to the Federal Funds rate, the ratio of Non Borrowed
to Total Reserves and the Romer and Romer (1989) index. Using data from the flexible
exchange rate era, we find that expansionary shocks to U.S. monetary policy lead to
sharp, persistent depreciations in U.S. nominal and real exchange rates as well as to sharp,
persistent increases in the spread between various foreign and U.S. interest rates. The
temporal pattern of the depreciation in U.S. nominal exchange rates following a positive
monetary policy shock is inconsistent with simple overshooting models of the type
considered by Dombusch (1976). We also find that U.S. monetary policy was less volatile
under fixed exchange rates than under floating exchange rates. Finally, we find less
evidence that monetary policy shocks had a significant impact on U.S. real exchange rates
under the Bretton Woods agreement.

Martin Eichenbaum
Department of Economics
Northwestern University
2003 Sheridan Road
Chicago Illinois 60208
1-708-491-8232




Charles Evans
Research Department
Federal Reserve Bank of Chicago
230 South LaSalle Street
Chicago Illinois 60604
1-312-322-5812

1. Introduction
This paper presents new empirical evidence that expansionary monetary pol­
icy shocks generate substantial, persistent depreciations in U.S. nominal and real
exchange rates. Our analysis builds on work by Stockman (1983), Mussa (1986),
Baxter and Stockman (1989), Backus and Kehoe (1992) and Meltzer (1992) who
have documented key features of international business cycles. Unlike these au­
thors, we do not focus on unconditional correlations. Instead, we ask how interest
rates and exchange rates (nominal and real) respond to a specific impulse, namely
a shock to monetary policy.
We focus on conditional correlations because of the difficulty of interpreting
unconditional correlations in environments where agents are subject to multiple
sources of uncertainty.

Consider for example the widely noted fact that real

exchange rates have been substantially more volatile after the collapse of the
Bretton Woods agreements. This fact is equally consistent with Mussa’s (1986)
view that it reflects the importance of sluggish price adjustment and monetary
policy shocks or Stockman’s (1988) view that it reflects the greater variance of
real shocks in the floating exchange rate era. In addition, the relative volatility
of real exchange rates across the different regimes could also be rationalized, in
principle, by models like those of Grilli and Roubini (1991,1992) and Schlagenhauf
and Wrase (1992a,b) who emphasize the liquidity effects of monetary policy shocks
on interest rates and exchange rates.
In this paper we concentrate on isolating a measure of shocks to monetary
policy and ask how interest rates and exchange rates respond to these shocks.
By focusing on these types of conditional moments of the data we follow recent
work on the effects of monetary policy shocks on interest rates in closed economy




2

settings.1 This literature is relevant to our study for two reasons. First, it makes
concrete the importance of explicitly adopting identifying assumptions to measure
the exogenous component (if any) of changes in monetary policy. Second, several
studies in this literature argue that widely used measures of monetary policy
shocks such as innovations to high order monetary aggregates are inconsistent with
the actual operating procedures of the Federal Reserve system. Moreover the use
of these measures lead to misleading inference regarding the interest rate effects
of policy shocks (see Bernanke and Blinder (1992), Christiano and Eichenbaum
(1992a,b) and Strongin (1992)). In this paper we use the measures of monetary
policy shocks proposed by these authors (orthogonalized innovations to the Federal
Funds rate and the ratio of Non Borrowed to Total Reserves) as well as the index
proposed by Romer and Romer (1989)) to study the effects of monetary policy on
exchange rates.
Our main results can be summarized as follows. First, we find that expansion­
ary shocks to U.S. monetary policy are followed by sharp, persistent declines in
U.S. interest rates, and sharp, persistent increases in the spread between various
foreign and U.S. interest rates. Second, we find that the same shocks lead to
sharp, persistent depreciations in U.S. nominal and real exchange rates. Taken
together these first two findings cast doubt on international Real Business Cycle
(RBC) models in which money is introduced simply by adding cash-in-advance
constraints or a transactions role for money. A generic implication of these mod­
els is that positive shocks to the money supply cause domestic interest rates to
rise and lead to a fall in the spread between foreign and domestic interest rates.
(See Schlagenhauf and Wrase (1992a,b) for a discussion of this point). As such
these findings provide support for the model economies considered by Grilli and
1For a review of t literature see Christiano and Eichenbaum (1992a).
his




3

Roubini (1991,1992) and Schlagenhauf and Wrase (1992a,b) which allow for liq­
uidity effects.
Third, we strongly reject the null hypothesis that the maximal depreciation of
the U.S. nominal (or real) exchange rate in response to a positive money supply
shock occurs in the period of the monetary shock. This finding is inconsistent with
the theoretical implications of simple overshooting models of the sort considered
by Dornbusch (1976). In conjunction with our finding that monetary policy shocks
lead to a rise in the spread between foreign and U.S. interest rates, this finding
is also inconsistent with the hypothesis of uncovered interest rate parity. This is
because the larger interest rate differential induced by a positive shock to monetary
policy shock is

not

expected to be offset by expected future appreciations in the

dollar.
Fourth, we find that U.S. monetary policy was less volatile under fixed ex­
change rates than under floating exchange rates. This is consistent with the no­
tion that the fixed exchange regime imposed constraints on U.S. monetary policy.
Finally, we find that there is somewhat less evidence that monetary policy shocks
had a significant impact on U.S. real exchange rates during the fixed exchange
rate era. Taken together these last two findings are consistent with the notion
that increased volatility of monetary policy directly contributed to the increased
volatility of real exchange rates in the post Bretton Woods era. However it does
not bear on the empirical plausibility of the hypothesis that real shocks were also
more volatile in the post Bretton Woods era.
The remainder of this paper is organized as follows. Section 2 discusses the
measures of shocks to monetary policy that are used in our analyses. Section 3
presents findings using data from the post Bretton Woods era. Section 4 briefly
considers the Bretton Woods era. Finally, section 5 contains some concluding




4

remarks.

2. Measuring Shocks to Monetary Policy

To measure the effects of shocks to monetary policy, we must take a stand on
an empirical measure of those shocks. In this paper we consider three measures:
orthogonalized components of the innovation to the ratio of Non Borrowed to Total
Reserves, orthogonalized components of the innovation to the Federal Funds rate,
and the Romer and Romer (1989) index of monetary policy contractions.
The basic strategy underlying the first two measures is to identify monetary
policy shocks with the disturbance term in a regression equation of the form:

K = c(nt + €vt.
)

Here

Vt

is the time t setting of the monetary authority’s policy instrument, £ is

a linear function,
when

Vt

(i)

Qt

is set and

is the information set available to the monetary authority

evt

is a serially uncorrelated shock that is orthogonal to the

elements of Qt. To rationalize interpreting

tv t

as a policy disturbance one must

view (1) as the monetary authority’s decision rule for setting

Vt.

The first two

measures of policy shocks which we use correspond to different specifications of
Vt

and

Ctt .

Conditional on this specification, the dynamic response of a variable to

a monetary policy shock is measured by the regression coefficients of the variable
on current and lagged values of the residuals to equation (1).
This procedure is asymptotically equivalent to computing the impulse response
function of a variable to a particular shock in an appropriately identified Vector
Autoregression (VAR). Denote the set of variables in a VAR by
fi* includes the lagged values of




Zt

Z t.

Assume that

as well as the time t values of a subset of the
5

variables in

Z t,

which we denote by

spond to a Wold ordering in which

The identifying assumptions in (1) corre­

X t.
Xt

is (causally) prior to V This corresponds
*.

to the assumption that the monetary authority sets

Vt

seeing lagged values of all

the components of Z t and the current values of X t . The ‘shock’ to monetary policy
is the component of the innovation to

Vt

which is orthogonal to innovations in

X t.

This basic strategy for identifying shocks to monetary policy has been used by
a variety of authors. For example Barro (1977), Mishkin (1983), Litterman and
Weiss (1985) and King (1991) identify shocks to monetary policy with innovations
to monetary aggregates like Ml and M2, i.e.

Vt

is set to Ml* or M2t which the

Federal Reserve Board is assumed to choose on the basis of time t-1 information.
In addition to using Ml and M2, Leeper and Gordon (1992) also consider the
innovation to the base (MO) as a measure of monetary policy shocks.
We do not pursue the general scheme of identifying policy shocks with orthogonalized innovations to broad monetary aggregates for a number of reasons.
First, Strongin (1992) argues that they rely on assumptions which are simply
counter factual in light of the actual operating procedure of the Federal Reserve
Board. Second, a number of authors such as Gordon and Leeper (1992) show, in
closed economy contexts, that so measured, monetary policy shocks are followed
by

in c re a se s

in short term interest rates. Christiano and Eichenbaum (1992a)

show that when the analysis is redone using the measure of money that is di­
rectly affected by open market operations, Non Borrowed Reserves (NBR), one
reaches precisely the opposite conclusion, namely that expansionary monetary
policy shocks are followed by sharp, persistent

d ec re a se s

in short term nominal

interest rates. Moreover Eichenbaum and Evans (1992) show that positive inno­
vations to NBR are followed by increases in higher order monetary aggregates.
Christiano and Eichenbaum (1992b) argue that these results indicate that NBR




6

innovations primarily reflect exogenous shocks to monetary policy, while innova­
tions to broader monetary aggregates primarily reflect shocks to demand.
While Christiano and Eichenbaum (1992a,b) use NBR as the monetary ag­
gregate in their analysis, Strongin (1992) argues that an even sharper measure
of exogenous shocks to the money supply can be obtained measuring

Vt

by the

ratio of NBR to Total Reserves. We denote this ratio by NBRX. 2 In our context,
working with NBR’s or NBRX leads to qualitatively similar results.3 For this
reason, here we report results only for NBRX.
Our second measure of shocks to monetary policy is motivated by arguments
in McCallum (1983), Sims (1992) and Bernanke and Blinder (1992) that, at least
relative to high-order monetary aggregates like Ml and M2, orthogonalized shocks
to the Federal Funds rate are a better measure of shocks to monetary policy
than orthogonalized shocks to the stock of money. Finally, our third measure of
monetary policy shocks is motivated by results in Romer and Romer (1989) who
use historical methods to identify specific periods in which the Federal Reserve
Board initiated contractionary changes in monetary policy. Given the widespread
attention that their index of monetary policy has received we wish to document
the robustness of our results to their measure of policy shocks.

3. Empirical Results: The Flexible Exchange Rate Era

This section examines the dynamic response of nominal and real exchange
rates to U.S. monetary policy shocks in the flexible exchange rate period. In
deciding which variables to include in our empirical analysis, we are forced to
2Strongin actually measures V« as NBR</(Total Reserves),_i while we use NBR|/(Total
Reserves)(.This has virtually no impact on our r s l s
eut.

3Eichenbaum and Evans (1992) provide evidence to substantiate t i claim.
hs




7

deal with the following trade-off. In the interest of minimizing omitted variable
bias, we would like to include as many variables in our VAR’s as possible. On the
other hand, we must confront the problem of parameter profligacy. Specifically,
if we include

k

lags of n variables in a VAR, then we must estimate

k

x n2 free

parameters. Clearly, one’s degrees of freedom rapidly disappear and inference
becomes impossible. One must impose some restrictions on the variables to be
included in the VAR. In light of this, when we included a measure of foreign output
and a measure of short term foreign interest rates in a VAR, we did not include
a measure of a high order foreign monetary aggregate. Doing so seemed to have
little added value given our objective of identifying shocks to U.S. monetary policy.
Moreover, Sims (1992) argues that shocks to foreign monetary policy are better
captured by orthogonalized shocks to foreign interest rates than by orthogonalized
shocks to broad foreign monetary aggregates.
The results reported in this section are based on monthly data covering the
sample period 1974:1-1990:5. The appendix contains a detailed description of our
data. Eichenbaum and Evans (1992) document the qualitative robustness of our
results to breaking the sample in 19S5:1 (the approximate date of the Louvre
agreement). All VAR’s were estimated using 6 lags of all variables.4
We consider five nominal (spot) exchange rates, e for, For = {Yen, Deutchmark
(DM), Lira, French Franc (FF), U.K. Pound (PD)}. Here e f or denotes the number
of foreign currency units needed to buy one U.S. dollar at time t. Defined in this
way, an increase in e for corresponds to an

a p p re c ia tio n

addition we consider five real exchange rates,

of the US dollar. In

, For = {Yen, DM, Lira, FF,

PD} where e£°r is defined as
4Our lag length was selected based on evidence regarding the s r a correlation in the VA R
eil
error term, as measured by the Q s a i t c discussed in Doan (1990), as well as robustness of
ttsi
inference to higher order l g .
as




8

r t

The variables

Pt

and

P tFor

denote the time t U.S. and foreign price levels, respec­

tively. Given this definition, an increase in e£°r denotes an appreciation in the
real U.S. exchange rate.
3.1 Empirical Results: NBRX Based Measures of Policy Shocks.
In this subsection we consider results from two VAR’s. In the first, the interest
rate variable is the

d ifferen ce

between the level of foreign and U.S. short term

nominal interest rates. This VAR is of interest for two reasons. First, a variety
of authors like Meese and Rogoff (1983) consider empirical models where it is the
difference between foreign and U.S. interest rates that is relevant for exchange
rate determination. Second, this system captures, in a parsimonious way a subset
of our key results. In the second VAR, the level of foreign and US interest rates
enter as separate variables. This allows us to (i) document the robustness of the
basic features of results based on the more parsimonious VAR, and (ii) examine
the impact of policy shocks on the level of domestic and foreign interest rates, per
se.
We begin by reporting results from a five variable VAR that includes U.S.
industrial production (F), the U.S. Consumer Price Level (P), the ratio of NBR
to Total Reserves

(NBRX),

short term interest rates

a measure of the difference between U.S. and foreign

( R For — R u s ),

short term foreign interest rate,
rate taken from the
interest rate,




Ru s,

R For,

and the real exchange rate, e^or. The

was measured using a short term interest

I n te r n a tio n a l F in a n c ia l S ta tis tic s

tape. The short term U.S.

was measured using the three month Treasury Bill rate.

9

Rows 1 and 2 of Figure 1 report a subset of the dynamic impulse response
functions emerging from the estimated VAR. These were calculated assuming a
Wold ordering of {Y, P, NBRX,

R For — R u s , e p °T}.

This corresponds to the

assumption that the contemporaneous portion of the U.S. monetary authority’s
feedback rule for setting (NBRX)* involves

Yt

and

Pt

but not

R For — R u s

or

e^°r. So here a monetary shock is measured as the component of the innovation in
(N BRX)t

that is orthogonal to innovations in

Yt

and

P t.

5 Columns 1 through 5 of

Figure 1 report results for the Japanese, German, Italian, French and U.K. cases,
respectively. The solid lines in Rows 1 and 2 report the dynamic response of R [ ° r —
R .ys

and

e^ °T,

respectively, to a one standard deviation shock to monetary policy.

The dashed lines denote a one standard deviation band about point estimates of
the coefficients in the impulse response functions.6 We redid our analysis replacing
the real exchange rate with the corresponding nominal exchange rate in the VAR.
The resulting dynamic response functions of

R For — R ^ s

to a monetary policy

shock are virtually identical to those reported in Row 1. Row 3 of Figure 1
reports the dynamic response functions of e for to the policy shock.
A number of important results emerge from Figure 1. First, a positive shock
to monetary policy leads to a persistent, significant increase in

R For — R Y S ,

i.e.

an increase in the spread between short term foreign and U.S. interest rates. For
example, according to Figure 1 the initial impact of a one standard deviation
(roughly 1.18%) shock to ( N B R X ) t is a {28, 38, 27, 22, 44} basis point change
in

— R .ys :

F or

=

Y en, D M ,L ira,F F ,P D },

respectively.7 Second, the

5No restrictions are imposed on the lagged components of the monetary authority’ feedback
s
rl.
ue
6These were computed using the method described i Doan (1990), example 10 1 using
n
.,
500 draws from estimated asymptotic distribution of the vector autoregressive c e f c e t and
ofiins
covariance matrix of the innovations.
7The actual shock to (NBRX)* equals 1.16%, 1.21% 1.18%, 1.19% and 1.18% fo the case i
r
n
which Japan, Germany, I a y France and the U.K. are the foreign country included i the VAR.
tl,
n




10

estimated impulse response functions of nominal and real exchange rates are very
similar. This is consistent with the well known fact that movements in real and
nominal exchange rates are highly correlated with each other (see for example
Mussa (1986)). Third, a positive shock to monetary policy leads to persistent de­
preciations in both ejfcr and e for. For example, the initial impact of a one standard
deviation (roughly 1.18%) shock to (N B R X ) t is a {0.28, 0.50, 0.42, 0.36, 0.28}
per cent fall in {e^*n, e^t , e^‘ra>
m t

}, respectively. In addition, the maximal

impact of the monetary shock on e^°T and e f 0T does not occur contemporaneously.
For example, the maximal impact on { e p n, e f m,e f,ro,e f F, e fD} equals {-1.98, 2.85, -2.59, -2.64, -2.18} per cent which occurs {22, 34, 37, 35, 39} months after
the monetary policy shock. This response pattern is difficult to reconcile with sim­
ple overshooting models of the sort considered by Dornbusch (1976) in which a
positive shock to monetary policy generates a large initial depreciation in nominal
(and real) exchange rates followed by subsequent appreciations. In these models,
uncovered interest rate parity holds so that the higher time t foreign nominal in­
terest rate must be offset by an expected appreciation of the dollar between time
t and time t+1. This prediction is clearly at variance with the impulse response
functions reported in Figure 1. There we see that, in response to a time t positive
monetary shock, R f° r —R ^ s rises and e for declines between time t and time t+1.
So the time t expected return on the foreign asset is higher for two reasons: (i)
the nominal return is higher and (ii) the foreign currency is expected to appreciate
between time t and time t+1. It seems difficult to reconcile these results with the
uncovered interest rate parity hypothesis. Instead, we think of them as reflecting
the widely documented statistical rejection of that hypothesis (see for example
Hodrick (1987)).
In principle, one could construct a variety of statistics to summarize the ‘shape’




11

of the impulse response functions as a way of characterizing the dynamic response
of exchange rates to policy shocks. For example one could ask whether the impulse
response function is identically equal to zero. We find it more revealing to consider
the average response of e^°r and e f or to a time t monetary shock over various time
horizons, say from time t + i to time t+ j. We denote these responses by HFor,R(i,j)
and HFor(i,j), respectively. In population these are equal to the average value of
coefficients i through j of the corresponding impulse response functions.
We cannot use the standard deviation bands about the estimated impulse re­
sponse functions in Figure 1 to formally test hypotheses about fiFor,R(i,j) and
MFor(i,j)- This is because each element in these bands summarizes the sampling
uncertainty in the corresponding element of the estimated impulse response func­
tion, not taking into account the covariance between different coefficients in the
impulse response functions. Consequently, they cannot be used to formally test
hypotheses involving the joint behavior of these coefficients.
To deal with these problems, we adopted the following procedure.

Let /?

and V denote the coefficients in a given VAR and the covariance matrix of the
corresponding innovations to the VAR, respectively. After estimating the VAR,
we proceeded as follows:
(1) First, we calculated a consistent estimate of the parameters governing the joint
asymptotic distribution of /? and V (see Doan (1990), example 10.1).
(2) Second, we drew a sample, /?£ and Vj*, from the estimated impulse response
functions of ej^r and e for, k =1, ...,500.
(3) Third, for each (B£,V£), we calculated the impulse response functions of e£°r
and e for to a monetary shock.
(4) Fourth, we calculated the sample values of fiFor,R(i,j) and f*For{i,j) in these




12

impulse response functions. Denote these by HFor,R(hi) and HFor(i,j)> {(i, j), =
(1,6), (7,12), (13-18), (19-24), (25-30), (31-36)}.
(5) Fifth, we calculated the standard deviations of (iFor,R(hj) and /Xfor(i,i).8
Table 1A reports the results of this procedure as applied to the five variable
VAR containing e£°r. Columns 1 through 5 report results for the case in which the
foreign country is Japan, Germany, Italy, France and the U.K., respectively. Row
1 reports the estimated correlation between the innovation to e £ fRand (N B R X )t.
Numbers in parentheses denote standard errors, while numbers in brackets denote
the significance level of the t test for the one sided hypothesis that the correla­
tion equals zero in population against the alternative that it is negative. Notice
that for every measure of the exchange rate, the estimated correlation is negative
and significantly different from zero. Rows 2 through 7 report the estimated val­
ues of f i F o r A h j M i h j ) = (1,6), (7,12),(13,18), (19,24), (25,30),and (31,36)},
respectively. Numbers in parentheses denote standard errors, while numbers in
brackets denote the significance level of the t test for the one sided hypothesis
that t*For,R{i,j) is equal to zero against the alternative that it is negative. Notice
that for each country, there exist a number of horizons corresponding to different
specifications of (i, j) for which this hypothesis can be rejected at conventional
significance levels. For Germany, France and Italy, the hypotheses can be rejected
for every specification of (i, j) at the 5% significance level. Consistent with Fig­
ure 1, these rejections are not the strongest for the early periods. For example,
according to our point estimates, e^*n drops on average by .53%, 1.16%, 1.53%,
1.67%, 1.61% and 1.39% in the first through sixth half year horizons after a posi­
tive monetary shock. The corresponding significance levels for the test that these
Alternatively, inference could be based on the empirical distribution function of these statis­
tics. In practice we found that inference was very robust to which procedure was adopted.




13

responses are zero in population attain their maximum value for the third half
year horizon, after which they decline. Row 8 reports the maximal impact of a
positive monetary policy shock on e for.9 In every case the point estimate of this
statistic is negative and exceeds (in absolute value)

hfot,r {1,6).

Also notice that

in every case we strongly reject the null hypothesis that the maximal impact is
equal to zero.
Row 9 reports the time to the maximal depreciation in the real exchange
rate following a policy shock.10 As can be seen, there is substantial uncertainty
about the exact time period when the maximal depreciation occurs. Still for every
country we can easily reject the null hypothesis that it occurs contemporaneously.
Table IB is the exact analog to Table 1A except that it is based on the five variable
VARs that include e for. As before, using nominal rather than real exchange rates
has very little impact on inference.
We now discuss the overall contribution of monetary shocks to the variability
of exchange rates. To this end, we computed the percentage of the variance of
the k step ahead forecast error that is attributable to monetary shocks. As k
goes to infinity, this corresponds to the percentage of the variance of exchange
rates that is due to monetary shocks. Row 10 of Tables 1A and IB reports
the average of this percentage over the 31 to 36 month horizon for real nominal
exchange rates, respectively. The estimated percentages range from a low of 18%
(U.K., nominal exchange rates) to a high of 43% (Germany, real). While there
is substantial sampling uncertainty associated with these point estimates, in the
case of Germany, Italy and France, one can easily reject the null hypothesis that
®Numbers in parenetheses denote standard errors, while numbers in brackets denote the
significance level of the t test for the hypothesis that the maximal impact is equal to zero.
10Numbers in parentheses denote standard errors, while numbers in brackets denote the sig­
nificance level of the t test for the hypothesis that the maximal depreciation occurs in the period
of the shock.




14

the percentage is zero, for either real or nominal exchange rates. The rejections
are more marginal for Japan and the U.K.
We now consider the results of analyzing a VAR in which foreign and U.S.
interest rates enter separately. Rows 1 through 3 of Figure 2 report results from
a seven variable VAR that includes US industrial production (Y ), the U.S. Con­
sumer Price Level (P),foreign output ( Y For), the foreign interest rate (RFor), the
ratio of NBR to TR (NBRX), the real exchange rate, e£or, and the three month
U.S. Treasury Bill rate, P f s . Impulse response functions were calculated assum­
ing a Wold ordering of {V, P, Y For, R Fot, N B R X , Ru s , e^or}. This corresponds to
the assumption that the contemporaneous portion of the US monetary authority’s
feedback rule for setting (N B R X ) t involves (FJ, Pt, Y For, R Fot) but not R%s or
e£°r. So here a monetary shock is measured as the component of the innovation in
( N B R X ) t that is orthogonal to innovations in Yt,Pt,YtFor and R For. Columns 1
through 5 report results for the Japanese, German, Italian, French and U.K. cases,
respectively. The solid lines in Rows 1, 2 and 3 report the dynamic response of
R ¥s , R Fo and e£jr , respectively, to a one standard deviation impulse to our mea­
t
sure of a monetary policy shock. The dashed lines denote one standard deviation
bands about point estimates of the coefficients in the impulse response functions.
We redid our analysis replacing the real exchange rate with the corresponding
nominal exchange rate in the VAR. The resulting dynamic response functions of
R ^ s and R Fo to a monetary policy shock are virtually identical to those reported
t
in Rows 1 and 2. Row 4 of Figure 2 reports the dynamic response functions of
e for to the policy shock.
Notice that, irrespective of which foreign country is included in the analysis, a
positive shock to U.S. monetary policy leads to a sharp, persistent decrease in the
US interest rate. In addition the shock leads to a persistent decline in all of the




15

foreign interest rates, except the U.K. The statistical significance of this decline
depends on which foreign country is included in the analysis. In all cases, though,
the declines in R ^ s exceeds the corresponding decline in R [ 0 so that, consistent
T
with Figure 1, the shock leads to an increase in R f 0T — R ^ s . Also as before, a
positive monetary shock leads to pronounced, persistent depreciations in real and
nominal U.S. exchange rates. Perhaps not surprisingly given the large number
of variables in the VAR (and the correspondingly large number of parameters
that must be estimated), the impulse response functions of e^°r and e f or are less
precisely estimated than in the five variable VAR systems underlying Figure 1.
Tables 2A and 2B, which are the exact analogs to Tables 1A and IB, confirm
this impression. In particular, we find substantially less evidence against the null
hypotheses that HFor,Fi(i,j) and HFor{i,j) are equal to zero in population. Still for
each country there exists at least one specification of (i,j) for which one can reject,
at the 10% significance level (or better), these null hypotheses. Moreover, for every
country, we can reject at the 5% significance level or better, the null hypothesis
that the maximal deprecation of e for to a positive money shock is zero. Finally,
for all countries except for Japan, one can reject, at the 5% significance level, the
null hypothesis that the correlation between the innovations to NBRX and e£°r
(or e f or) is equal to zero. For Japan this hypothesis can be rejected at the 7%
significance level.
Row 10 of Tables 2A and 2B reports the average percentage of the variance
of the forecast error over the 31 to 36 month horizon for real and nominal ex­
change rates that is attributable to monetary shocks. Notice that the estimated
percentages are lower than those emerging from the five variable VAR and now
range from a low of 8% (France, real exchange rates) to a high of 14% (Italy,
nominal exchange rates). In addition the standard errors of these statistics are




16

substantially larger than before.
3.2 Empirical Results: Federal Funds Rate Based Measures of Policy Shocks

In this subsection we display results obtained measuring monetary policy as
an orthogonalized component of the innovation to the Federal Funds rate. Rows 1
through 4 of Figure 3 reports results from a seven variable VAR that includes data
on U.S. industrial production (F), the U.S. Consumer Price Level (P ), foreign out­
put ( y Fot), the foreign interest rate (RFor), the Federal Funds rate (F F t),the ratio
of NBR to TR (NBRX), and the real exchange rate, e£°r. Impulse response func­
tions were calculated assuming a Wold ordering of {F, P, Y For, R For, F F , N B R X ,
e^or}. This Wold ordering corresponds to the assumption that the contemporane­
ous portion of the U.S. monetary authority’s feedback rule for setting FF t involves
(Yt, Pt, Y For, R For) but not N B R X t or e £ f. Columns 1 through 5 of Figure 3
reports results for the Japanese, German, Italian, French and U.K. cases, respec­
tively. The solid lines in Rows 1, 2 and 3 report the dynamic response of N B R X t ,
R For and e£°r, respectively, to a one standard deviation shock to monetary policy.
The dashed lines denote one standard deviation bands about point estimates of
the coefficients in the impulse response functions. We redid our analysis replacing
the real exchange rate with the corresponding nominal exchange rate in the VAR.
The resulting dynamic response functions of NBRX and R For to a monetary pol­
icy shock are virtually identical to those reported in Rows 1 and 2. Row 4 of
Figure 3 reports the dynamic response functions of eFor to the policy shock.
Our results here are consistent with those of the previous subsection. Consis­
tent with the presence of a strong liquidity effect, Figure 3 reveals that a positive
shock to the Federal Funds rate generates sharp, persistent declines in NBRX.
Notice also that a negative monetary policy shock (a positive shock to the Federal




17

Funds rate) is associated with persistent appreciations in nominal and real U.S. ex­
change rates. For example, according to Figure 3, the initial impact of an approxi­
mately 60 basis point positive shock to the Federal Funds rate is a {0.31,0.46,0.40,
0.38,0.15} percent rise in {e^fn, egt , e ^ ro, e £ f, e£f }, respectively. As before, the
M
maximal impact of the monetary shock on e£°r and e for does not occur contem­
poraneously. For example, the maximal impact on { e p n, e f A/,e f,ra’e fF, e fD} of
an approximately 60 basis point shock to FFt is a {1.76, 1.88, 1.66, 1.78, 1.33}
per cent rise that occurs {21, 28, 28, 28, 28} months later. As in subsection 3.1,
the dynamic response functions of real and nominal exchange rates to monetary
shocks are very similar.
It is important to note that the dynamic responses of e £ f and e f or to a policy
shock are estimated more precisely now than when orthogonalized innovations
to NBRX are used as the measure of monetary policy shocks. This can be seen
informally by comparing the relevant standard deviation bands in Figures 2 and
3. This impression is confirmed by Tables 3A and 3B. These are the exact analogs
to tables 2A and 2B, constructed using the VARs underlying Figure 3.
A number of key results emerge from these tables. First, Row 1 of Tables 3A
and 3B reveal that innovations to the Federal Funds rate are positively correlated
with innovations to nominal and real exchange rates. The null hypothesis that
either of these correlations equals zero in population can be easily rejected for the
Japanese, German, Italian and French cases. The rejection is more marginal for
the U.K. Second, there is very strong statistical evidence that monetary policy
shocks affect real and nominal exchange rates. For example, except for the U.K.
case, the null hypothesis that fiFor,R(i,j) equals zero can be rejected, at the 4%
significance level or better, for all six specifications of (i,j). In the U.K. case we
can reject this hypothesis at the 5% significance level in 4 out of 6 specifications




18

of (i, j). From row 8 of Tables 3A and 3B we see that the null hypotheses that
the maximal impact of a monetary policy shock on e£°r and e for equal zero can
be strongly rejected. Finally, as before, we find substantial evidence that the
maximal effect of a policy shock does not occur contemporaneously. From Row
9 of Tables 3A and 3B we see that for every country one can easily reject the
null hypothesis that the maximal effect on a policy shock on e£°r and e f or occurs
contemporaneously.
Row 10 of Tables 3A and 3B reports the average percentage of the variance of
the forecast error over the 31 to 36 month horizon for real and nominal exchange
rates that is attributable to monetary shocks. Here two key results emerge. First,
for all countries, except the U.K., monetary shocks are estimated to account for
over 20% of the variance of real and nominal exchange rates. Second, there is
less sampling uncertainty with this measure of monetary shocks than with NBRX
based measures. Specifically, for all countries (except for the U.K.) we can easily
reject the null hypothesis that monetary shocks do not account for any of the
variance in real or nominal exchange rates. So once we move to Federal Funds
based measures of policy shocks, we find substantial evidence that an important
percentage of the variability of exchange rates can be attributed to policy shocks,
even with the seven variable VAR’s.
3.3 Empirical Results: Measuring Monetary Shocks Using the Romer and Romer
(1989)Index
In this subsection we report results obtained using the Romer and Romer
(1989) index of monetary policy. Figure 4 report results obtained from a VAR
that includes U.S. industrial production (V), the U.S. Consumer Price Level (P ),
foreign output (YrFor), the foreign interest rate (RFor), the ratio of NBR to TR




19

(NBRX), the real exchange rate, e^or, and the Federal Funds rate, FF. In addi­
tion, the VAR includes the Romer and Romer (1989) index of monetary policy.
Specifically, we consider a VAR for the vector of variables Zt :

Zt = A(L)Zt- 1 + 0 (L )d t + €t.

(2)

Here A(L) and 0(L) are one sided polynomials in the lag operator L, and the
vector Zt equals [Yt,Pf,Y for, R for,NBRXt, e£°r, FF]'. The variable dt denotes the
time t value of the Romer and Romer index . This variable equals one for the
month at which a Romer and Romer episode begins. It is equal to zero otherwise.
The response of Zt+k to a time t Romer and Romer monetary contraction (dt =
1, dt+jt = 0 f o r A > 0) is given by the coefficient on Lk in the polynomial
:
[/ - A (L))-'0(L)."
Columns 1 through 5 of Figure 4 report results for the Japanese, German,
Italian, French and the U.K. cases, respectively. Rows 1, 2, 3 and 4 display the
dynamic impulse response functions of FFt, N B R X t, R f or and e£°r to the onset
of a Romer and Romer episode. The dashed lines denote one standard deviation
bands about point estimates of the coefficients in the impulse response functions.
We redid our analysis replacing the real exchange rate with the corresponding
nominal exchange rate in the VAR. The resulting dynamic response functions of
FFt, NBRXt and R f 0T to a Romer and Romer shock are virtually identical to those
reported in Rows 1, 2 and 3. Row 5 of Figure 4 reports the dynamic response
functions of ef°r to the policy shock.
Rows 1 and 2 of Figures 4 provide corroborating evidence that the Romer and
Romer dummy variables do indeed correspond to monetary policy contractions.
u The dates of the Romer and Romer (1989) episodes are 1974:4, 1978:8 and 1979:10. Since
our sample ends after theirs, we included a dummy variable for the period 1988:8 suggested by
Oliner and Rudebusch (1992).




20

In particular, a unit increase in the Romer and Romer index is associated with a
sharp, persistent increase in the Federal Funds rate and a decrease in NBRX. No­
tice that the maximal increase in the Federal Funds rate and the maximal decrease
in NBRX do not occur at the time of the change in the index. Instead both occur
six months later. The initial change in the Federal Funds rate equals roughly 50
points. Six months later the Federal Funds rate is almost 300 basis points higher
than it was initially. Evidently Romer and Romer episodes correspond to large
monetary contractions, relative to the types of shocks considered in subsection 3.1
and 3.2. Our previous results indicated that we reached very similar conclusions
whether we use NBRX or Federal Funds rate based measures of monetary shocks.
In light of this, it is not surprising that the dynamic impulse responses functions
of NBRX and the Federal Funds rate to a change in the Romer and Romer index
appear to be mirror images of each other.
The fact that the peak effect of a change in the Romer and Romer on NBRX
and the Federal Funds rate occurs with a six month delay helps explain the dy­
namic response functions of e£°r and e for. The initial response of real and nom­
inal exchange rates is either very close to zero or slightly negative. However in
all cases, after six months, real and nominal exchange rates undergo persistent
appreciations. This is consistent with the results of subsections 3.1 and 3.2. The
large responses of FFt, R for,e£°r and e for reflect the magnitude of the Romer
and Romer episodes. The main difference between the results reported here and
those of subsections 3.1 and 3.2 is that the dynamic responses of e for and e ^J
are now measured with much less precision. This is not surprising in light of the
small number of observations on monetary contractions used here. Tables 4A and
4B provide additional evidence on this point. Rows 1 through 6 of these tables
report the estimated values of /XFor,fl(i» j ) and HFor{i,j), {(i, j) = (1,6), (7,12),




21

(13,18), (19, 24), (25,30) and (31,36)}, respectively. Notice that here we cannot
reject, at conventional significance levels, the null hypothesis that these are equal
to zero. Still, even with this method of measuring policy shocks, we can reject, at
the 7% and 8% significance levels, the null hypotheses that the maximal impact
on the real and nominal exchange rate is equal to zero (see Row 7 of Tables 4A
and 4B, respectively). Finally, with the exception of the U.K., there is strong
evidence that the maximal effect of a policy shock on real and nominal exchange
rates does not occur in the initial period of the shock (see row 8 of Table 4A and
4B, respectievly).

4. Money Supply Shocks, Interest Rates and Exchange Rates in the Pre-Bretton
Woods Era.

In this section we examine the effects of shocks to monetary policy on real
exchange rates during a subset of ihe Bretton Woods era. While exchange rates
were ‘fixed’ before 1971, there were various episodes in which the exchange rates
of different countries were revalued. Because of this we redid the analysis of
sections 3.1 and 3.2 using different sub samples of 1959:1 - 1971:7. For each foreign
country we chose the sub sample so that the nominal exchange rate was constant.
Specifically, we used data over the period {1959:7-1971:7, 1961:1-1969:10, 1960:101971:9,1959:7-1969.7, 1959:7-1967:11} for the Japanese, German, Italian, French
and U.K. cases respectively.
Our analysis focuses on two key questions: (i) What were the relative mag­
nitudes of monetary policy shocks during the flexible and fixed exchange rate
regimes? (ii) Was the relationship between monetary policy shocks and real ex­
change rates different in the flexible and fixed exchange rate regimes?




22

Recall from section 3 that, from a statistical point of view, our sharpest results
were obtained using a Federal Funds based measure of monetary policy shocks.
Not surprisingly, the differences between the flexible and fixed exchange rate pe­
riod emerge most starkly when we use this measure of policy shocks.12
Consider Figure 5 which is the exact analog to the first three rows of Figure 3.
Here we report results based on a seven variable VAR that includes US industrial
production (Y), the U.S. consumer Price Level (P), foreign output (YFor), the
foreign interest rate (RFor), the Federal Funds rate (FF), the ratio of NBR to
Total Reserves (NBRX), and the real exchange rate (e£or). As in subsection 3.2,
the impulse response functions of R^5, R for, and e f 0T to a monetary shock (rows
1, 2 and 3, respectively) were calculated assuming a Wold ordering of {Y, P,
YFor, RFor, F F , NBRX, e^or}, so that a monetary policy shock is measured as
the component of the innovation to the Federal Funds rate that is orthogonal to
innovations in Y(, P t, Y for, and R for. Table 5 is the analog to Table 3A and
reports the results of implementing the testing procedures described in section 3.
Three key results emerge here. First, the standard deviation of shocks to mon­
etary policy is estimated to be much smaller in the Bretton Woods era. Specif­
ically, during the flexible exchange rate period, the standard deviation of our
Federal Funds based measure of policy is approximately 60 basis points. The cor­
responding standard deviation in the fixed exchange rate period equals 24,13, 22,
17, and 14 basis points for the Japanese, German, Italian, French and U.K. cases,
respectively.13 So, according to this metric, monetary policy is much more volatile
in the floating exchange rate regime. Second, while still positive (except for Italy)
12Some care must exercised in interpreting these results as there is less reason to believe that
the Federal Reserve Board followed a tight policy of targeting the Federal Funds rate in the
pre-1974 period. For a discussion of this point see Goodfriend (1991).
13These estimates are not the same because each is generated from a VAR with a different set
of variables in it corresponding to the different specifications of Y f or, e^°r and Rf or considered.




23

the correlation between the innovation to the Federal Fund rate and the real ex­
change rate appears to be smaller and less significant in the fixed exchange rate
regime (see row 1 of table 5). This provides corroborating evidence for the view
that the link between real exchange rates and monetary policy shocks was weaker
in the fixed exchange rate regime. Third, the standard error bands in Figures
3 and 5 indicate a weaker dynamic response of real exchange rates to monetary
policy shocks in the fixed exchange rate regime. This impression is confirmed by
the statistical results summarized in Tables 3A and 5. Recall that, for the floating
exchange rate period, we found overwhelming evidence against the null hypothesis
that fiFor,p.(i,j) is equal to zero, for almost all specifications of (i, j). In sharp
contrast, according to Table 5, for the fixed exchange rate period, we cannot reject
this hypothesis for any specification of (i, j) in the Japanese, Italian, French and
U.K. cases. There is substantial evidence that the U.S. real exchange rate vis a vis
Germany appreciated following a contractionary monetary policy shock. At the
same time it should be emphasized that the magnitude of the response is much
smaller (roughly one fourth after correcting for the smaller size of the shock) in
the fixed exchange rate period compared to the floating exchange rate period.
Row 10 of Table 5 reports the average percentage of the variance of the forecast
error over the 31 to 36 month horizon for the real exchange rate. The percentages
range from a low of 7% for Germany to a high of 29% for Italy. However, with
the exception of Italy, we cannot reject, at the 10% level, the null hypothesis that
monetary shocks account for any of the forecast error variance of the real exchange
rate.14 Recall from Table 3A that this hypothesis could be rejected for all five
countries in the flexible exchange rate regime.
14Recall that for Italy we obtained the unusual result that a positive innovation to the Federal
Funds rate leads to a depreciation of the real U.S. exchange rate.




24

Next consider the difference between the flexible and fixed exchange rate pe­
riods with the NBRX based measure of shocks to monetary policy. Here the
difference is less stark, although the basic pattern of our results is unaffected.
Figure 6 is the analog to the first three rows of figure 2. Here we report results
from a seven variable VAR that includes US industrial production (Y), the U.S.
consumer Price Level (P), foreign output (YFor), the foreign interest rate (RFor),
the ratio of NBR to Total Reserves (NBRX), the real exchange rate (e£or), and the
three month U.S. Treasury Bill (R175). As in subsection 3.1, the impulse response
functions of R f5, R for, and e for to a monetary shock (rows 1, 2 and 3, respec­
tively) were calculated assuming a Wold ordering of {Y, P, Y For, RFor, NBRX,
Ru s , epj°r}, so that a monetary policy shock is measured as the component of the
innovation to NBRX that is orthogonal to innovations in Yt, P t, Y for, and R for.
Table 6 is the exact analog to Table 2A.
Two key points emerge. First, there is less of a contrast between the flexible
and fixed exchange rate periods when we move to the NBRX based measures of
shocks to monetary policy. Specifically, with the exception of Italy, the pattern of
a statistically significant, negative correlation between innovations to NBRX and
the real exchange rate is present in both periods. In addition Tables 2A and 6
indicate that positive innovations to monetary policy cause statistically significant
appreciations in U.S. real exchange rates. However according to Tables 2A and 6
indicate that in both exchange regimes, NBRX based measures of monetary policy
shocks do not account for a significant percentage of the forecast error variance
of real exchange rates. Recall that Second, the standard deviation of shocks to
monetary policy is estimated to be much smaller in under fixed exchange rates.
Specifically, during the flexible exchange rate period, the standard deviation of our
NBRX based measure of policy shocks is approximately 1.18%. The corresponding




25

standard deviation in the fixed exchange rate period equals .68%, .35%, .66%,
.44%, .33% for the Japanese, German, Italian, French and U.K. cases, respectively.
Viewing the results of this section as a whole we conclude that (i) irrespective
of which measure of policy is used, U.S. monetary policy was less volatile in the
fixed exchange rate period, and (ii) there is mixed evidence on whether monetary
policy shocks had a significant impact on U.S. real exchange rates during the fixed
exchange rate era.

5. Conclusion

This paper investigated the effects of shocks to monetary policy on nominal
and real U.S. exchange rates. We found strong evidence that expansionary policy
shocks lead to significant, persistent depreciations in exchange rates, both nominal
and real. In addition, we found that these policy shocks contribute significantly
to the overall variability of U.S. exchange rates in the post Bretton Woods era.
At the same time though, these shocks do not explain the majority of movements
in U.S. exchange rates. Monetary policy was important but it was by no means
the sole determinant of changes in real exchange rates. Our results are entirely
consistent with the notion that real changes which affect the relative prices of
the different goods produced by different countries were at least as important as
monetary policy in the process of exchange determination.




26

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the Historical Properties of Business Cycles,’ American Economic Review,
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2. Barro, Robert J. (1977), ‘Unanticipated Money Growth and Unemployment
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Lates,’ manuscript, Northwestern University.

g

10. Goodfriend, Marvin (1991), ‘Interest Rates and the Conduct of Monetary
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4
14. King, Robert (1991), ‘Money and Business Cycles,’ manuscript, University
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27

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7
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28

28. Stockman, Alan C. (1988) ‘Real Exchange Rate Variability Under Pegged
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29

figure 1
Dynamic Response Functions: Orthogonalized Shock i NB RX
n
5 Variable System*

BSSP I T
AI O N S

RA-U
JPR9

DUSHMR-EL
ETCEAKRA

LR-EL
IARA

FACRA
RN-EL

PUDRA
ON-EL

YN
E

DUSHMR
ETCEAK

LR
IA

FAC
RN

PUD
ON

PRET
ECN

PRET
ECN

YNRA
E-EL

‘
Column 1 displays the dynamic e f c o an orthogonalized i n v t o i NBRX on the d f e e c between Japanese and U.S. i t r s r t s (RJAP-RUS), the r a U.S.-Japan
fet f
noain n
ifrne
neet a e
el
Columns 2 through 5 do the same f rGermany, Iay France, and t e U K , r s e t v l .
o
tl,
h .. epciey


exchange r t (YEN-REAL) and the nominal U.S.-Japan exchange r t (YEN).
ae
ae


PRET
ECN

PRET
ECN

B SSPI T
AI ONS

B S SP I T
AI ONS

Figure 2
Dynamic Response Functions: Orthogonalized Shock i NB RX
n
7 Variable System*

‘
Column
e
fet f
noain n
neet ae
neet a e
el
 1 displays th dynamic e f c o an orthogonalized i n v t o i NBRX on the U.S. i t r s r t (RUS), the Japanese i t r s r t (RJAP), the r a U.S.-Japan exchange
r t (YEN-REAL) and the nominal U.S.-Japan exchange r t (YEN). Columns 2 through 5 do the same f rGermany, Iay France, and the U K , r s e t v l .
ae
ae
o
tl,
.. epciey


PRET
ECN

BSSPIT
AI ON S

PRET
ECN

Figure 3
Dynamic Response Functions: Orthogonalized Shock i Federal Funds Rate
n
7 Variable System*

PUD
ON

DUSHMR
ETCEAK

PRET
ECN

YN
E

7

2
5

6

2
1

3
6

6

2
1

3
8

6

2
1

3
6

6

2
1

3
6

Digitized for•Column 1 displays the dynamic e f c o an orthogonalized i
FRASER
fet f
nnovation i the Fed Funds r t on NBRX, the Japanese I t r s r t (RJAP), the r a U.S.-Japan exchange r t (YEN
n
ae
neet ae
el
ae
http://fraser.stlouisfed.org/ nominal U.S.-Japan exchange r t (YEN). Columns 2 through 5 do the same f rGermany, Iay France, and the U K , r s e t v l .
REAL) and the
ae
o
tl,
.. epciey
Federal Reserve Bank of St. Louis

Figure 4
Dynamic Response Functions: Romer and Romer Shock
8 Variable System*

FED FUNDS RATE

FED FUNOS RATE

FED FUNDS RATE

FEO FUNDS RATE

B SS
AI

FED FUNDS RATE

7

2
4

4
1

NBRX

6

2
1

3
8

8

2
1

3
8

6

NBRX

NBRX

2
1

3
6

NBRX

NBRX

1.6

PRET
ECN

06
.
00
.
•.
08
•.
16

•.
24
•.
32
-.
40
•.
46
•.
S6
RE
GR

YNRA
E-EL

6
2
1
3
8
DUSHMR-EL
ETCEAKRA

5

RR
FA

RITA

1
9
RK
U

3
3

BSSP I T
AI O N S

RA
JP

2
1
LR-EL
IA R A

3
6

6
2
1
3
6
FACRA
RN-EL

PUDRA
ON-EL

PRET
ECN

6

2
3

4
0

6

2
3

4
0

5
1
9 3
3
OUSHMR
ETCEAK

5

1
9

3
3

5

5

1
9

3
3

S

1
9
3
3
FAC
RN

5

1
9 3
3
PUO
ON

PRET
ECN

6

1
9

3
3

•Column
fet
ae
neet ae
el
ae
 1 displays the dynamic e f c ofa Romer and Romer shock on the Fed Funds r t , NBRX, the Japanese i t r s r t (RJAP), the r a U.S.-Japan exchange r t (YEN'
REAL) and
ae
o
tl,
.. epciey
http://fraser.stlouisfed.org/ the nominal U.S.-Japan exchange r t (YEN). Columns 2 through 5 do the same f rGermany, Iay France, and the U K , r s e t v l .
Federal Reserve Bank of St. Louis

Figure 5
Dynamic Response Functions: Orthogonalized Shock i Federal Funds Rate
n
7 Variable System, Fixed Exchange Rate Period*

NBRX

NBRX

NBRX

NBRX

NBRX

•.^i11 n r i n 1111 n 11 r 111 r
15 n 11111 1111 11111 m m r ~
m
S
1
9
3
3

YNRA
E-EL

DUSHMR-EL
ETCEAKRA

‘
Column 1 displays the dynamic e f c o an orthogonalized i n v
fet f
n o ation i the Fed Funds r t on NBRX, the Japanese i t r s r t (RJAP), and t e r a U.S.-Japan exchange r t
n
ae
neet ae
h el
ae


(YEN-REAL). Columns 2 through 5 do the same f rGermany, Iay France, and the U K , r s e t v l .
o
tl,
.. epciey


PRET
ECN

BSSPIT
AI ONS

BSSP I T
AI O N S

Figure 6
Dynamic Response Functions: Orthogonalized Shock i NBRX
n
7 Variable System, Fixed Exchange Rate Period*

•Column 1 displays the dynamic e f c o an orthogonalized i n v t o i NBRX on the U.S. i t r s r t (RUS), the Japanese i t r s r t (RJAP), and the r a U.S.-Japan e '
fet f
noain n
neet ae
neet ae
el
x
Columns 2 through 5 do the same f rGermany, Iay France, and the U K , r s e t v l .
o
tl,
.. epciey


change r t (YEN-REAL).
ae


Table 1A
N B R X Based Measure of Policy Shocks, 5 Variable System
Real Exchange Rates
Dynamic Response Functions

Japan

Germ anv

Iay
tl

France

U.K.

()
1

CORR(NBRX,EXCH)
standard e r r
ro
significance l v l
ee

-0.1534
(0.069)
[.1]
003

-0.2665
(0.070)
[.
0 000]

-0.2207
(0.069)
[.0]
001

-0.2086
(0.067)
[0.001]

-0.1662
(0.069)
[.0]
008

()
2

1-6 months
standard e r r
ro
significance l v l
ee

-0.5343
(0.347)
[.6]
002

-0.9135
(0.303)
[.
0 001]

-0.6824
(0.294)
[.
0 010]

-0.6681
(0.289)
[0.010]

-0.4302
(0.286)
[.6]
006

()
3

7-12 months
standard e r r
ro
significance l v l
ee

-1.1615
(0.605)
[.2]
008

-1.2471
(0.538)
[.
0 010]

-0.9771
(0.464)
[.
0 018]

-0.9107
(0.502)
[0.035]

-0.5798
(0.449)
[.9]
008

()
4

13-18 months
standard e r r
ro
significance l v l
ee

-1.5311
(0.747)
[.2]
000

-1.5459
(0.689)
[0.012]

-1.1255
(0.583)
[.2]
007

-1.0409
(0.626)
[0.0 8
4]

-0.7084
(0.560)
[.
0 103]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

-1.6677
(0.873)
[.2]
008

-2.0291
(0.817)
[.
0 007]

-1.5575
(0.704)
[.1]
004

-1.621
(0.726)
[0.013]

-1.0423
(0.659)
[.5]
007

()
6

25-30 months
standard e r r
ro
significance l v l
ee

-1.6114
(0.961)
[.4]
007

-2.4044
(0.974)
[.
0 007]

-2.0227
(0.831)
[0.0 8
0]

-2.1586
(0.862)
[0.006]

-1.4889
(0.761)
[.2]
005

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-1.3945
(1.017)
[.8]
005

-2.5753
(1.158)
[.
0 013]

-2.3238
(0.991)
[.1]
000

-2.411
(1.030)
[0.010]

-1.839
(0.876)
[.1]
008

()
8

Max Impact
standard e r r
ro
significance l v l
ee

-1.9808
(0.921)
[.1]
006

-2.846
(1.422)
[.
0 023]

-2.5921
(1.340)
[0.0 7
2]

-2.6444
(1.278)
[0.019]

-2.1842
(1.123)
[.2]
006

()
9

Max Month
standard e r r
ro
significance l v l
ee

22.438
(10.417)
[.1]
006

34.346
(9.520)
[.
0 000]

36.932
(8.773)
[0.000]

35.362
(8.615)
[0.000]

39.228
(9.419)
[.0]
000

37.52
(14.877)
[0.0 2
1]

26.1525
(15.034)
[.8]
002

Variance Decompositions
(10)

31-36 Months
standard e r r
ro
significance l v l
ee




23.0162
(13.640)
[.9]
002

42.9168
(15.713)
[0.006]

38.1219
(15.481)
[0.014]

Table IB
N B R X Based Measure of Policy Shocks, 5 Variable System
Nominal Exchange Rates
Dynamic Response Functions

Japan

Germanv

Iay
tl

France

U.K.

()
1

CORR(NBRX,EXCH)
standard e r r
ro
significance l v l
ee

-0.1505
(0.074)
[.2]
000

-0.265
(0.064)
[0.000]

-0.2213
(0.065)
[.0]
000

-0.2057
(0.070)
[.
0 002]

-0.1378
(0.070)
[.
0 024]

()
2

1-6 months
standard e r r
ro
significance l v l
ee

-0.509
(0.351)
[.7]
004

-0.852
(0.302)
[0.002]

-0.6042
(0.288)
[.1]
008

-0.6474
(0.291)
[.1]
003

-0.2486
(0.270)
[.7]
018

()
3

7-12 months
standard e r r
ro
significance l v l
ee

-1.0369
(0.607)
[.4]
004

-1.089
(0.566)
[0.027]

-0.8262
(0.493)
[.4]
007

-0.851
(0.532)
[.
0 055]

-0.1317
(0.431)
[0.380]

()
4

13-18 months
standard e r r
ro
significance l v l
ee

-1.3947
(0.773)
[.3]
006

-1.3894
(0.707)
[0.025]

-0.9919
(0.585)
[.4]
005

-1.0201
(0.640)
[.
0 055]

-0.2277
(0.544)
[0.338]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

-1.5885
(0.896)
[.3]
008

-1.9387
(0.826)
[0.010]

-1.5346
(0.681)
[.1]
002

-1.6841
(0.742)
[.
0 012]

-0.6297
(0.609)
[0.151]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

-1.5954
(0.974)
[.5]
001

-2.4131
(0.984)
[0.007]

-2.1136
(0.816)
[.
0 005]

-2.3513
(0.875)
[0.004]

-1.1617
(0.658)
[0.039]

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-1.4399
(1.011)
[.7]
007

-2.6739
(1.182)
[0.012]

-2.4836
(0.991)
[.
0 006]

-2.7288
(1.050)
[0.005]

-1.5534
(0.721)
[0.016]

()
8

Max Impact
standard e r r
ro
significance l v l
ee

-1.9472
(0.988)
[.
0 024]

-2.9456
(1.546)
[0.028]

-2.7958
(1.369)
[.
0 021]

-3.0006
(1.345)
[0.013]

-1.8895
(0.917)
[0.020]

()
9

Max Month
standard e r r
ro
significance l v l
ee

23.692
(10.971)
[.1]
005

35.604
(9.114)
[0.000]

37.712
(7.563)
[0.0 0
0]

37.15
(7.217)
[0.000]

39.642
(7.958)
[.
0 000]

38.4735
(15.879)
[0.015]

18.7524
(12.428)
[0.131]

Variance Decompositions

(10)

31-36 Months
standard e ror
r
significance l v l
ee




22.0842
(13.901)
[.1]
012

41.0213
(16.271)
[0.012]

38.7669
(15.135)
[.
0 010]

Table 2 A
N B R X Based Measure of Policy Shocks, 7 Variable System
Real Exchange Rates
Dynamic Response Functions

Japan

Germ anv

Iay
tl

France

U.K.

standard e r r
ro
significance l v l
ee

-0.1031
(0.070)
[.6]
009

-0.2511
(0.071)
[0.000]

-0.2256
(0.067)
[.0]
000

-0.2124
(0.066)
[.0]
001

-0.1494
(0.073)
[.2]
001

()
2

1-6 months
standard e r r
ro
signif c n e l v l
i a c ee

-0.3224
(0.343)
[.7]
014

-0.4128
(0.277)
[0.068]

-0.4806
(0.258)
[.3]
001

-0.4213
(0.264)
[.5]
006

-0.1315
(0.264)
[.
0 309]

()
3

7-12 months
standard e r r
ro
sg
i nificance l v l
ee

-0.8108
(0.582)
[.8]
002

-0.0741
(0.472)
[0.438]

-0.363
(0.424)
[.9]
016

-0.2084
(0.502)
[.3]
039

0.0511
(0.407)
[.5]
050

()
4

13-18 months
standard e r r
ro
sgi
i n ficance l v l
ee

-0.9413
(0.724)
[.9]
007

-0.3522
(0.556)
[.
0 263]

-0.3829
(0.470)
[.0]
028

-0.0341
(0.564)
[.
0 476]

-0.1464
(0.492)
[.
0 383]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

-1.0861
(0.823)
[.9]
003

-0.8246
(0.583)
[0.079]

-0.6988
(0.500)
[.8]
001

-0.3798
(0.576)
[.
0 255]

-0.5019
(0.530)
[.7]
012

()
6

25-30 months
standard e r r
ro
significance l v l
ee

-1.106
(0.902)
[.1]
010

-1.0779
(0.628)
[0.043]

-0.9269
(0.537)
[0.0 2
4]

-0.6155
(0.583)
[.
0 145]

-0.9284
(0.566)
[.
0 050]

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-0.9796
(0.973)
[.5]
017

-1.072
(0.717)
[0.067]

-0.9454
(0.581)
[0.0 2
5]

-0.6677
(0.608)
[.
0 136]

-1.1861
(0.611)
[.
0 026]

()
8

Max Impact
standard e r r
ro
significance l v l
ee

-1.5207
(0.844)
[.3]
006

-1.3257
(0.655)
[0.021]

-1.1981
(0.467)
[.0]
005

-1.0094
(0.468)
[.
0 016]

-1.3773
(0.665)
[.
0 019]

()
9

Max Month
standard e r r
ro
significance l v l
ee

21.128
(11.591)
[.3]
004

24.154
(13.561)
[0.037]

23.7
(14.001)
[0.0 5
4]

18.586
(15.573)
[.1]
016

36.144
(9.904)
[.0]
000

8.3719
(6.448)
[.9]
014

10.6872
(7.814)
[0.171]

()
1

CORR(NBRX,EXCH)

Variance Decompositions
(10)

31-36 Months
standard e r r
ro
significance l v l
ee




13.2631
(10.677)
[.1]
024

12.893
(8.830)
[0.144]

13.5346
(10.324)
[0.190]

Table 2B
N B R X Based Measure of Policy Shocks, 7 Variable System
Nominal Exchange Rates
Dynamic Response Functions

Japan

Germ anv

Iay
tl

France

U-K.

()
1

CORR(NBRX,EXCH)
standard e r r
ro
significance l v l
ee

-0.1029
(0.072)
[.7]
005

-0.2482
(0.067)
[0.000]

-0.235
(0.067)
[.
0 000]

-0.2133
(0.070)
[.
0 001]

-0.1233
(0.070)
[.
0 038]

()
2

1-6 months
standard e r r
ro
significance l v l
ee

-0.2352
(0.327)
[.3]
026

-0.3663
(0.266)
[0.084]

-0.4347
(0.246)
[.3]
008

-0.4514
(0.278)
[.5]
002

-0.0168
(0.254)
[.
0 474]

()
3

7-12 months
standard e r r
ro
significance l v l
ee

-0.5529
(0.541)
[.5]
013

0.013
(0.466)
[.
0 511]

-0.3046
(0.431)
[.4]
020

-0.2706
(0.532)
[.
0 305]

0.3766
(0.433)
[0.8081

()
4

13-18 months
standard e r r
ro
significance l v l
ee

-0.658
(0.638)
[.5]
011

-0.2916
(0.585)
[0.309]

-0.3613
(0.534)
[.4]
029

-0.1289
(0.612)
[.
0 417]

0.2083
(0.534)
[0.652]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

-0.8626
(0.721)
[.1]
016

-0.8141
(0.641)
[0.102]

-0.726
(0.603)
[.1]
014

-0.5152
(0.606)
[.
0 198]

-0.2153
(0.570)
[0.353]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

-0.9893
(0.776)
[.
0 101]

-1.1174
(0.689)
[0.053]

-1.0397
(0.678)
[.
0 063]

-0.7933
(0.607)
[.
0 096]

-0.6589
(0.583)
[0.129]

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-0.952
(0.825)
[.
0 124]

-1.1527
(0.741)
[0.060]

-1.1387
(0.776)
[.
0 071]

-0.8627
(0.615)
[0.080]

-0.8833
(0.586)
[.
0 066]

()
8

Max Impact
standard e r r
ro
significance l v l
ee

-1.3113
(0.683)
[.
0 028]

-1.3684
(0.688)
[.
0 023]

-1.3533
(0.835)
[.
0 053]

-1.1458
(0.513)
[0.013]

-1.0782
(0.512)
[.
0 018]

()
9

Max Month
standard e r r
ro
significance l v l
ee

23.534
(12.008)
[.
0 025]

26.562
(13.117)
[0.021]

27.494
(13.333)
[.
0 020]

22.592
(15.698)
[0.075]

34.224
(11.038)
[.
0 001]

8.6336
(7.004)
[0.218]

9.4064
(6.134)
[0.125]

Variance Decompositions

(10)

31-36 Months
standard e r r
ro
significance l v l
ee




11.1799
(9.497)
[0.2 9
3]

13.2704
(9.329)
[0.155]

13.743
(9.601)
[0.152]

Table 3 A
Fed Funds Rate Based Measure of Policy Shocks, 7 Variable System
Real Exchange Rates
Dynamic Response Functions

Japan

Germany

Iay
tl

France

U.K-

()
1

CORR(FFJEXCH)
standard e r r
ro
significance l v l
ee

0.1571
(0.069)
[.1]
001

0.2647
(0.068)
[0.0 0
0]

0.2312
(0.073)
[.0]
001

0.22
(0.068)
[.
0 001]

0.1088
(0.072)
[.6]
005

()
2

1-6 months
standard e r r
ro
significance l v l
ee

0.6232
(0.312)
[.2]
003

0.9089
(0.274)
[.0]
001

0.7893
(0.234)
[.0]
000

0.7495
(0.268)
[.
0 003]

0.4952
(0.251)
[.
0 0241

()
3

7-12 months
standard e r r
ro
significance l v l
ee

1.1793
(0.453)
[.0]
005

1.0776
(0.424)
[.
0 006]

1.0403
(0.373)
[.
0 003]

1.0212
(0.453)
[.
0 012]

0.639
(0.406)
[0.058]

()
4

13-18 months
standard e r r
ro
significance l v l
ee

1.4154
(0.516)
[.
0 003]

1.194
(0.505)
[.
0 009]

0.9703
(0.430)
[.
0 012]

0.935
(0.511)
[0.034]

0.6287
(0.469)
[.9]
000

()
5

19-24 months
standard e r r
ro
significance l v l
ee

1.5062
(0.580)
[.0]
005

1.3233
(0.560)
[.
0 009]

1.0328
(0.476)
[.1]
005

1.2034
(0.538)
[0.013]

0.7842
(0.474)
[.
0 049]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

1.4504
(0.657)
[.1]
004

1.4913
(0.634)
[.
0 009]

1.2632
(0.537)
[.0]
009

1.3988
(0.613)
[.
0 011]

1.027
(0.478)
[.1]
006

()
7

31-36 months
standard e r r
ro
significance l v l
ee

1.2799
(0.726)
[.
0 039]

1.6026
(0.729)
[0.014]

1.4282
(0.616)
[0.0 0
1]

1.5027
(0.695)
[.
0 015]

1.1549
(0.491)
[.0]
009

()
8

Max Impact
standard e r r
ro
significance l v l
ee

1.755
(0.686)
[0.0 5
0]

1.8838
(0.818)
[0.011]

1.6647
(0.643)
[0.0 5
0]

1.778
(0.725)
[.
0 007]

1.3298
(0.475)
[.0]
003

()
9

Max Month
standard e r r
ro
significance l v l
ee

20.734
(9.840)
[0.0 8
1]

28.452
(15.056)
[.
0 029]

28.12
(14.840)
[0.0 9
2]

28.38
(14.165)
[.
0 023]

27.688
(13.074)
[.
0 017]

24.7293
(11.733)
[.
0 035]

16.9572
(10.052)
[.9]
002

Variance Decompositions
(10)

31-36 Months
standard e r r
ro
significance l v l
ee




21.6427
(10.456)
[0.0 9
3]

26.5427
(11.456)
[.
0 021]

25.3986
(10.093)
[0.0 2
1]

Table 3B
Fed Funds Rate Based Measure of Policy Shocks, 7 Variable System
Nominal Exchange Rates
Dynamic Response Functions

Janan

Germanv

Iay
tl

France

U-K.

()
1

CORR(FF£XCH)
standard e r r
ro
significance l v l
ee

0.1472
(0.071)
[.1]
009

0.2679
(0.064)
[0.000]

0.2437
(0.069)
[0.0 0
0]

0.2181
(0.073)
[.
0 002]

0.0989
(0.072)
[0.0 5
8]

()
2

1-6 months
standard e r r
ro
significance l v l
ee

0.5994
(0.306)
[.2]
005

0.8844
(0.265)
[0.000]

0.7217
(0.241)
[.0]
001

0.7425
(0.273)
[.
0 003]

0.4018
(0.260)
[0.0 1
6]

()
3

7-12 months
standard e r r
ro
significance l v l
ee

1.154
(0.422)
[.0]
003

1.0122
(0.439)
[0.011]

0.935
(0.422)
[.
0 013]

0.9537
(0.467)
[.
0 021]

0.4017
(0.406)
[0.161]

()
4

13-18 months
standard e r r
ro
significance l v l
ee

1.348
(0.465)
[.
0 002]

1.1207
(0.527)
[0.017]

0.8694
(0.502)
[.
0 042]

0.9046
(0.542)
[0.0 8
4]

0.3968
(0.458)
[0.193]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

1.4525
(0.531)
[.
0 003]

1.2851
(0.581)
[0.013]

0.9764
(0.535)
[.3]
004

1.2504
(0.577)
[0.015]

0.5627
(0.466)
[0.113]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

1.4475
(0.611)
[.
0 009]

1.5154
(0.644)
[0.009]

1.2955
(0.597)
[.1]
005

1.5178
(0.654)
[0.010]

0.8044
(0.464)
[0.042]

()
7

31-36 months
standard e r r
ro
significance l v l
ee

13
.5
(0.682)
[.
0 024]

1.7018
(0.733)
[0.010]

1.5441
(0.695)
[.
0 013]

1.6805
(0.754)
[0.013]

0.9283
(0.472)
[0.025]

()
8

Max Impact
standard e r r
ro
significance l v l
ee

1.7202
(0.641)
[.
0 004]

2.0057
(0.880)
[0.011]

1.7763
(0.893)
[.
0 023]

1.956
(0.811)
[.
0 008]

1.1208
(0.441)
[0.006]

()
9

Max Month
standard e r r
ro
significance l v l
ee

21.962
(10.951)
[.2]
003

32.342
(14.450)
[0.013]

33.43
(13.394)
[0.006]

31.214
(13.427)
[0.010]

28.054
(13.883)
[.
0 022]

26.7491
(12.145)
[0.028]

11.5707
(7.933)
[0.145]

Variance Decompositions

(10)

31-36 Months
standard e
rror
significance l v l
ee




22.908
(10.853)
[0.035]

25.9663
(11.208)
[0.021]

23.1556
(10.250)
[0.024]

Table 4 A
Romer and Romer Policy Shocks, 8 Variable System
Real Exchange Rates
Dynamic Response Functions

Japan

Germanv

Iay
tl

France

U-K.

()
1

1-6 months
standard e r r
ro
significance l v l
ee

-0.2221
(3.252)
[.2]
057

-0.8979
(2.703)
[0.630]

0.21
(2.569)
[.6]
047

1.6952
(2.734)
[.6]
028

1.9819
(2.563)
[.2]
020

()
2

7-12 months
standard e r r
ro
significance l v l
ee

3.5563
(5.922)
[.
0 274]

1.2126
(4.818)
[.
0 401]

3.6529
(4.590)
[.1]
023

4.9778
(5.333)
[.7]
015

2.8181
(4.625)
[.7]
021

()
3

13-18 months
standard e r r
ro
significance l v l
ee

4.8741
(5.759)
[.9]
019

0.5203
(4.948)
[0.458]

3.2519
(4.216)
[.2]
020

2.7843
(5.026)
[.9]
020

0.815
(4.823)
[.
0 4331

()
4

19-24 months
standard e r r
ro
significance l v l
ee

5.6292
(5.307)
[.4]
014

1.4961
(4.625)
[0.3 3
7]

3.6175
(3.864)
[.7]
015

3.0102
(4.674)
[.6]
020

1.1015
(4.749)
[.0]
048

()
5

25-30 months
. standard e r r
ro
significance l v l
ee

5.8686
(5.261)
[.
0 132]

3.0898
(4.727)
[0.257]

4.6161
(4.127)
[.3]
012

4.7997
(4.741)
[.5]
016

2.9253
(4.723)
[.6]
028

()
6

31-36 months
standard e r r
ro
signif
icance l v l
ee

5.7854
(5.461)
[0.1 5
4]

4.6728
(5.148)
[.
0 182]

5.688
(4.636)
[.1]
010

6.1384
(5.158)
[.1]
017

4.4119
(4.766)
[.7]
017

C)
7

Max Impact
standard e r r
ro
signif
icance l v l
ee

9.0819
(5.544)
[0.0 1
5]

7.9303
(5.128)
[0.061]

8.5931
(5.095)
[.4]
006

9.8147
(5.730)
[.4]
003

7.9368
(4.502)
[.3]
009

()
8

Max Month
standard e r r
ro
significance l v l
ee

24.452
(13.865)
[.3]
009

31.886
(17.297)
[0.033]

29.89
(17.004)
[.3]
009

28.114
(18.579)
[.6]
005

22.496
(18.700)
[.
0 115]




Table 4B
Romer and Romer Policy Shocks, 8 Variable System
Nominal Exchange Rates
Dynamic Response Functions

Japan

Germanv

Iay
tl

France

HJL

()
1

1-6 months
standard e r r
ro
significance l v l
ee

-0.3857
(3.102)
[.5]
050

-1.1982
(2.672)
[0.6 3
7]

0.0224
(2.550)
[.9]
047

1.4963
(2.618)
[0.284]

0.907
(2.456)
[.5]
036

()
2

7-12 months
standard e r r
ro
significance l v l
ee

2.7511
(5.781)
[.1]
037

0.6485
(4.969)
[0.4 8
4]

3.7961
(4.773)
[.1]
023

4.08
(5.117)
[.
0 213]

-0.1356
(4.325)
[.
0 513]

()
3

13-18 months
standard e r r
ro
significance l v l
ee

4.028
(5.705)
[.4]
020

-0.1906
(5.286)
[0.5 4
1]

3.3762
(4.558)
[.2]
029

1.9085
(5.229)
[0.358]

-2.0435
(4.670)
[0.669]

()
4

19-24 months
standard e r r
ro
significance l v l
ee

4.9427
(5.283)
[.7]
015

0.7415
(5.074)
[0.442]

3.8983
(4.328)
[.8]
014

2.366
(5.177)
[0.324]

-1.1687
(4.399)
[0.605]

()
5

25-30 months
standard e r r
ro
significance l v l
ee

5.5808
(5.199)
[.4]
012

2.4363
(5.203)
[0.320]

5.1283
(4.695)
[.
0 137]

4.6209
(5.502)
[0.201]

1.1879
(4.147)
[0.3 7
8]

()
6

31-36 months
standard e r r
ro
significance l v l
ee

5.9273
(5.546)
[.
0 143]

4.2423
(5.611)
[0.225]

6.3582
(5.334)
[.
0 117]

6.5352
(6.111)
[0.142]

3.0301
(3.979)
[0.223]

()
7

Max Impact
standard e r r
ro
significance l v l
ee

8.8048
(5.710)
[.
0 062]

8.0523
(5.705)
[0.079]

9.5084
(6.007)
[.
0 057]

10.3899
(6.792)
[0.063]

6.141
(3.896)
[.
0 058]

()
8

Max Month
standard e r r
ro
significance l v l
ee

26.446
(15.061)
[.
0 040]

35.004
(16.884)
[0.019]

33.088
(16.745)
[.
0 024]

32.632
(18.061)
[0.035]

25.532
(18.935)
[.
0 089]




Table 5
Fed Funds Rate Based Measure of Policy Shocks, 7 Variable System
Real Exchange Rates, Fixed Exchange Rate Period
Dynamic Response Functions

Japan

Germany

Iay
tl

France

U.K.

()
1

CORR(FF^XCH)
standard e r r
ro
significance l v l
ee

0.1214
(0.083)
[.7]
001

0.1963
(0.103)
[0.028]

-0.0574
(0.087)
[.4]
075

0.0544
(0.087)
[.6]
026

0.0632
(0.099)
[.6]
023

()
2

1-6 months
standard e r r
ro
significance l v l
ee

0.09
(0.069)
[.9]
005

0.0574
(0.026)
[.
0 013]

-0.062
(0.072)
[.0]
086

-0.0143
(0.039)
[.4]
062

-0.0244
(0.040)
[.2]
079

()
3

7-12 months
standard e r r
ro
significance l v l
ee

0.0842
(0.103)
[.0]
026

0.0572
(0.034)
[.
0 044]

-0.1366
(0.117)
[.7]
088

0.0001
(0.064)
[.0]
050

0.0326
(0.051)
[.6]
021

()
4

13-18 months
standard e r r
ro
significance l v l
ee

0.0995
(0.130)
[.2]
023

0.0754
(0.044)
[0.043]

-0.2813
(0.182)
[.3]
099

0.0168
(0.080)
[0.4 7
1]

0.0765
(0.067)
[.
0 126]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

0.0684
(0.141)
[.1]
034

0.0797
(0.064)
[0.1 5
0]

-0.355
(0.248)
[.2]
094

-0.0001
(0.123)
[0.5 0
0]

0.0552
(0.081)
[.
0 247]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

0.0154
(0.139)
[.5]
046

0.0857
(0.096)
[0.1 7
8]

-0.3254
(0.310)
[.5]
083

0.0007
(0.161)
[0.4 8
9]

0.0186
(0.090)
[.1]
048

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-0.0134
(0.141)
[.3]
058

0.0839
(0.139)
[0.274]

-0.2505
(0.376)
[.4]
077

-0.0135
(0.200)
[.
0 527]

-0.0042
(0.098)
[.1]
057

()
8

Max Impact
standard e r r
ro
significance l v l
ee

0.2285
(0.119)
[.2]
008

0.191
(0.314)
[.
0 271]

0.1124
(0.165)
[.
0 248]

0.1594
(0.260)
[.7]
020

0.1398
(0.088)
[.5]
006

()
9

Max Month
standard e r r
ro
significance l v l
ee

10.59
(10.502)
[0.1 7
5]

22.76
(17.125)
[0.092]

14.44
(17.262)
[.
0 201]

20.722
(15.199)
[0.0 6
8]

16.514
(9.324)
[.
0 038]

8.9259
(6.938)
[.
0 198]

20.3442
(12.445)
[.0]
012

Variance Decompositions
(10)

31-36 Months
standard e r r
ro
significance l v l
ee




15.765
(11.875)
[.8]
014

6.7015
(4.763)
[0.159]

28.6339
(15.310)
[0.061]

Table 6
N B R X Based Measure of Policy Shocks, 7 Variable System
Real Exchange Rates, Fixed Exchange Rate Period
Dynamic Response Functions

JaDan

Germ anv

Iay
tl

France

U.K.

()
1

CORR(NBRXJEXCH)
standard e r r
ro
significance l v l
ee

-0.1482
(0.079)
[.3]
001

-0.2107
(0.101)
[.
0 019]

0.0457
(0.093)
[.
0 698]

-0.1516
(0.089)
[.4]
005

-0.1346
(0.104)
[.9]
007

()
2

1-6 months
standard e r r
ro
significance l v l
ee

-0.1579
(0.064)
[.0]
007

-0.0457
(0.027)
[0.046]

0.0433
(0.077)
[.
0 714]

-0.0284
(0.039)
[.3]
022

-0.0004
(0.039)
[.9]
045

()
3

7-12 months
standard e r r
ro
significance l v l
ee

-0.099
(0.092)
[.4]
011

-0.0471
(0.036)
[0.093]

0.0359
(0.122)
[.
0 615]

-0.0241
(0.062)
[.
0 349]

-0.0599
(0.051)
[.
0 118]

()
4

13-18 months
standard e r r
ro
significance l v l
ee

-0.1733
(0.114)
[.6]
005

-0.0485
(0.044)
[0.134]

0.1383
(0.176)
[.
0 784]

-0.1451
(0.097)
[.
0 067]

-0.0971
(0.067)
[0.074]

()
5

19-24 months
standard e r r
ro
significance l v l
ee

-0.1449
(0.128)
[.2]
018

-0.042
(0.057)
[0.2 0
3]

0.2378
(0.233)
[.
0 846]

-0.2815
(0.155)
[.
0 035]

-0.1455
(0.085)
[0.0 3
4]

()
6

25-30 months
standard e r r
ro
significance l v l
ee

-0.1218
(0.149)
[.
0 208]

-0.0503
(0.080)
[0.2 5
6]

0.2451
(0.298)
[.9]
075

-0.3137
(0.204)
[.
0 062]

-0.131
(0.109)
[0.115]

()
7

31-36 months
standard e r r
ro
significance l v l
ee

-0.1095
(0.175)
[.6]
026

-0.0524
(0.104)
[0.3 8
0]

0.2027
(0.389)
[.9]
069

-0.2208
(0.275)
[.
0 211]

-0.1327
(0.139)
[0.1 0
7]

()
8

Max Impact
standard e r r
ro
significance l v l
ee

-0.2841
(0.095)
[.
0 001]

-0.1619
(0.138)
[0.121]

-0.1203
(0.125)
[.
0 168]

-0.4264
(0.296)
[0.075]

-0.2596
(0.241)
[0.140]

()
9

Max Month
standard e r r
ro
significance l v l
ee

11.032
(10.070)
[.3]
017

22.442
(18.223)
[0.109]

13.264
(15.051)
[0.1 9
8]

28.46
(8.420)
[.
0 000]

28.15
(13.494)
[0.019]

9.5577
(5.270)
[.
0 070]

10.8567
(7.758)
[0.162]

Variance Decompositions

(10)

31-36 Months
standard e r r
ro
significance l v l
ee




13.0994
(9.770)
[.
0 180]

7.1687
(3.867)
[0.064]

4.6498
(4.736)
[0.326]

A ppendix
This appendix describes the data used in this study.
Nominal exchange rates:
The data are bilateral monthly average exchange rates between the U.S. dollar
and Japanese Yen, German Deutschemark, French Frank, Italian Lira, and United
Kingdom Pound. For the flexible exchange rate period, the data source i the
s
Federal Reserve Board database. For the fixed exchange rate period, the single
(nominal) exchange rate for each bilateral country i taken from International
s

Financial Statistics.
U.S. data:
The source for the following data i the Federal Reserve database: Industrial
s
Production index. Consumer Price index-Urban. Federal Funds rate, monthly
average of daily rates, 3 month Treasury b l rates, monthly average of daily
il
rates, Total Reserves. Nonborrowed Reserves with Extended Credit and Special
Borrowings.
Foreign data:
For each country (Japan. Germany, Italy, France and the United Kingdom),
the data source i the International Financial Statistics database. Industrial Pro­
s
duction (line 66) and Consumer Price Indices (line 64) are used to measure foreign
output and foreign price levels. The choice of foreign interest rate depended upon
availability over the sample period.
Japan:
Flexible period: Short-term money market rate
Fixed period: Short-term money market rate.
Germany:
Flexible period: Short-term money market rate
Fixed period: Long term bond rate.
France:
Flexible period: Short-term money market rate
Fixed period: Long-term bond rate.
Italy:
Flexible period: Short-term money market rate
Fixed period: Long term bond rate.
United Kingdom:
Flexible period: Short-term Treasury b l rate
il
Fixed period: Long term bond rate.*