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FEDERAL RESERVE BANK OF ST. LOUIS
The Practice of Central
Bank Intervention:
Looking Under the
Hood
Christopher J. Neely
T
here has been a long and voluminous literature about official intervention in foreign
exchange markets. Official intervention is
generally defined as those foreign exchange transactions of monetary authorities that are designed
to influence exchange rates, but can more broadly
refer to other policies for that purpose. Many papers
have explored the determinants and efficacy of
intervention (Edison, 1993; Sarno and Taylor, 2000)
but very little attention has been paid to the more
pedestrian subject of the mechanics of foreign
exchange intervention like choice of markets, types
of counterparties, timing of intervention during
the day, purpose of secrecy, etc. This article focuses
on the latter topics by reviewing the motivation
for, methods, and mechanics of intervention.
Although there apparently has been a decline in
the frequency of intervention by the major central
banks, reports of a coordinated G-7 intervention to
support the euro on September 22, 2000, remind
us that intervention remains an active policy instrument in some circumstances.
The second section of the article reviews foreign
exchange intervention and describes several methods by which it can be conducted. The third section
presents evidence from 22 responses to a survey on
intervention practices sent to monetary authorities.
TYPES OF INTERVENTION
Intervention and the Monetary Base
Studies of foreign exchange intervention generally distinguish between intervention that does
Christopher J. Neely is a senior economist with the Federal Reserve
Bank of St. Louis. The author thanks the monetary authorities of
Belgium, Brazil, Canada, Chile, the Czech Republic, Denmark, France,
Germany, Hong Kong, Indonesia, Ireland, Italy, Japan, Mexico, New
Zealand, Poland, South Korea, Spain, Sweden, Switzerland, Taiwan,
and the United States for their cooperation with this study. The
author thanks Michael Melvin and Paul Weller for discussions
about the survey and Hali Edison, Trish Pollard, and Lucio Sarno
for helpful comments. Mrinalini Lhila provided research assistance.
This article was originally published in Central Banking (November
2000, XI(2), pp. 24-37; <http://www.centralbanking.co.uk>).
or does not change the monetary base. The former
type is called unsterilized intervention while the
latter is referred to as sterilized intervention. When
a monetary authority buys (sells) foreign exchange,
its own monetary base increases (decreases) by the
amount of the purchase (sale). By itself, this type
of transaction would influence exchange rates in
the same way as domestic open market purchases
(sales) of domestic securities; however, many central banks routinely sterilize foreign exchange
operations—that is, they reverse the effect of the
foreign exchange operation on the domestic monetary base by buying and selling domestic bonds
(Edison, 1993). The crucial distinction between
sterilized and unsterilized intervention is that the
former constitutes a potentially useful independent policy tool while the latter is simply another
way of conducting monetary policy.
For example, on June 17, 1998, the Federal
Reserve Bank of New York bought $833 million
worth of yen (JPY) at the direction of the U.S.
Treasury and the Federal Open Market Committee.
In the absence of offsetting transactions, this
transaction would have increased the U.S. monetary base by $833 million, which would tend to
temporarily lower interest rates and ultimately
raise U.S. prices, depressing the value of the dollar.1 As is customary with U.S. intervention, however, the Federal Reserve Bank of New York also
sold an appropriate amount of U.S. Treasury
securities to absorb the liquidity and maintain
desired conditions in the interbank loan market.
Similarly, to prevent any change in Japanese
money market conditions, the Bank of Japan
would also conduct appropriate transactions to
offset the rise in demand for Japanese securities
caused by the $833 million Federal Reserve purchase. The net effect of these transactions would
be to increase the relative supply of U.S. government securities versus Japanese securities held by
the public but to leave the U.S. and Japanese
money supplies unchanged.
Fully sterilized intervention does not directly
affect prices or interest rates and so does not influence the exchange rate through these variables as
ordinary monetary policy does. Rather, sterilized
intervention might affect the foreign exchange
market through two routes: the portfolio balance
channel and the signaling channel. The portfolio
1
Empirically, it has been very difficult to establish the reaction of
exchange rates to changes in economic fundamentals.
M AY / J U N E 2 0 0 1
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balance channel theory holds that sterilized purchases of yen raise the dollar price of yen because
investors must be compensated with a higher expected return to hold the relatively more numerous
U.S. bonds. To produce a higher expected return,
the yen price of the U.S. bonds must fall immediately. That is, the dollar price of yen must rise. In
contrast, the signaling channel theory assumes
that official intervention communicates information about future monetary policy or the long-run
equilibrium value of the exchange rate.
Spot and Forward Markets for
Intervention
The previous example implicitly assumed that
the Federal Reserve Bank of New York conducted
its purchase of yen in the spot market—the market for delivery in two days or less. Intervention
need not be carried out in the spot market, however; it also may be carried out in the forward
market.2 Forward markets are those in which
currencies are sold for delivery in more than two
days. Because the forward price is linked to the
spot price through covered interest parity, intervention in the forward market can influence the
spot exchange rate.
To understand covered interest parity, consider the options open to an American bank that
has capital to be invested for one year. The bank
could lend that money at the interest rate on U.S.
dollar assets, earning the gross return of (1+itUSD)
on each dollar. Or, it could convert its funds to a
foreign currency (e.g., the euro), lend that sum in
the overnight euro money market at the euro
interest rate, and then convert the proceeds back
to dollars at the end of the year. If, at the beginning of the contract, the bank contracts to convert
the euro proceeds back to dollars, it will receive
1/Ft,t +365 dollars for each euro, where Ft,t +365 is
the euros-per-dollar forward exchange rate. The
gross return to each dollar through this second
strategy is
St
1 + iteuro ,
Ft ,t + 365
(
)
where St is the euros-per-dollar spot exchange rate
on day t. If the return to one strategy is higher
than the other, market participants will invest in
that strategy, driving its return down and the other
return up until the strategies have approximately
2
M AY / J U N E 2 0 0 1
equal return. Covered interest parity (CIP) is the
condition that the strategies have equal return:
(1)
(1 + i ) = F S (1 + i ) .
USD
t
t
euro
t
t ,t + 365
As equation (1) must approximately hold all
the time, intervention that changes the forward
exchange rate must also change the spot exchange
rate.3 For example, a forward purchase of euros
that raises Ft,t +365 must also raise St.
Forward market interventions—the purchase
or sale of foreign exchange for delivery at a future
date—have the advantage that they do not require
immediate cash outlay. If a central bank expects
that the need for intervention will be short-lived
and will be reversed, then a forward market intervention may be conducted discreetly, with no
effect on foreign exchange reserves data. For example, published reports indicate that the Bank of
Thailand used forward market purchases to shore
up the baht in the spring of 1997 (Moreno, 1997).4
Both the spot and forward markets may be
used simultaneously. A transaction in which a
currency is bought in the spot market and simultaneously sold in the forward market is known as
a currency swap. While a swap itself will have little effect on the exchange rate, it can be used as
part of an intervention. The Reserve Bank of
Australia (RBA) used the swaps market to sterilize
spot interventions. In these transactions, the spot
leg of the swap is conducted in the opposite direction to the spot market intervention, leaving the
sequence equivalent to a forward market intervention. The RBA uses the spot/swap combination
rather than an outright forward transaction
because the former permits more flexible implementation of the intervention.
2
Exchange rate markets and practices are described in detail in the
Bank for International Settlements Central Bank Survey of Foreign
Exchange and Derivatives Market Activity (1999).
3
Of course, equation (1) could continue to hold with a change in
itUSD or iteuro instead of Ft,t +365, but the interest rates are held fixed
by conditions in the U.S. and euro money markets, respectively.
4
Not all spot or forward market transactions are interventions, of
course. For example, to limit the costs of capital controls that made
it hard to hedge foreign exchange exposure, the Reserve Bank of
South Africa (RBSA) used to provide forward cover for firms with
foreign currency exposure. That is, it would buy dollars forward
from foreign firms with dollar accounts receivable and sell dollars
to foreign firms with dollar accounts payable in the future. As capital controls have been reduced, the RBSA has reduced its net open
position in the forward market.
FEDERAL RESERVE BANK OF ST. LOUIS
The Options Market and Intervention
The options market has also been used by
central banks for intervention. A European-style
call (put) option confers the right, but not the obligation, to purchase (sell) a given quantity of the
underlying asset on a given date. Usually, the
option contract specifies the price for which the
asset may be bought or sold, called the strike or
exercise price.
Monetary authorities seeking to prevent depreciation or devaluation of their currency may sell
put options on the domestic currency or call
options on the foreign currency.5 While the price
of options has no direct effect on spot exchange
rates, speculators often purchase put options
instead of shorting a weak currency. The writers
(sellers) of these put options attempt to hedge
their position by taking a long position in the
weak currency, adding to the downward pressure
on its price. By writing put options on the weak
currency—adding liquidity to the options market—the central bank provides dealers with a synthetic hedge; dealers need not go into the spot
market to take short positions in the weak currency. This arrangement creates the same type of
financial risk for the central bank—if the currency
is devalued—as would the direct purchase of the
weak currency in spot or forward markets. Like
forward market intervention, it does not, however,
require the monetary authority to immediately
expend foreign exchange reserves. In fact, the
strategy generates revenues upon the sales of the
options. The Bank of Spain reportedly used this
strategy of selling put options on the peseta to
fight devaluation pressures during 1993 (The
Economist, 1993), though the institution denied it
emphatically (The Financial Times, 1993).
In another intervention strategy using options,
the Banco de Mexico has employed sales of put
options on the U.S. dollar to accumulate foreign
exchange reserves since August 1, 1996 (Galan
Medina, Duclaud Gonzalez de Castillo, Garcia
Tames, 1997). The put options give the bearer the
right to sell dollars to the Banco de Mexico at a
strike price determined by the previous day’s
exchange rate, called the fix exchange rate. The
option may be exercised only if the peso has
appreciated over the last month, if the fix peso
price of dollars is no higher than a 20-day moving
average of previous strike prices. This restriction is
designed to prevent the Banco de Mexico from
having to buy dollars (sell pesos) during a period
of peso depreciation.
The sales of these put options may be considered foreign exchange intervention because they are
designed to prevent the necessity of intervention to
purchase dollar reserves that might affect the exchange rate in undesirable ways. Because the mechanism is totally passive—the public decides when to
exercise the options—the use of these options effectively lessens the signaling impact of Banco de Mexico purchases of foreign exchange reserves.
Indirect Intervention
Recall that although official intervention is
generally defined as foreign exchange transactions of monetary authorities that are designed to
influence exchange rates, it can also refer to other
(indirect) policies for that purpose. In addition to
direct transactions in various instruments, Taylor
(1982a, b) recounts a number of methods by which
countries intervene indirectly in foreign exchange
markets. For example, he reports that in the 1970s
governments manipulated the currency portfolio
of nationalized industries in France, Italy, Spain,
and the United Kingdom to influence exchange
rates. This practice was allegedly used to “disguise”
intervention, as was the French and Italian practice of transacting through undisclosed foreign
exchange accounts held at commercial banks.
There are innumerable methods of indirectly
influencing the exchange rate that do not fit in
the narrow definition of intervention as foreign
exchange transactions of monetary authorities
designed to influence exchange rates. These methods involve capital controls—taxes or restrictions
on international transactions in assets like stocks
or bonds—or exchange controls—the restriction of
trade in currencies (Dooley, Mathieson, and RojasSuarez, 1993; Neely, 1999), rather than transactions.
Sometimes such methods are substituted for more
direct foreign exchange intervention, especially by
the monetary authorities of countries without a
long history of free capital movements. For example,
Spain, Ireland, and Portugal introduced capital
controls—including mandatory deposits against
the holding of foreign currencies—in the exchange
rate mechanism (ERM) crises of 1992-93, in response to speculation against their currencies.
5
Blejer and Schumacher (2000) discuss the implications of the use of
derivatives for central banks’ balance sheets.
M AY / J U N E 2 0 0 1
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Table 1
Responding Monetary Authorities
Country/authority
Belgium
Brazil
Canada
Chile
Czech Republic
Denmark
France
Germany
Hong Kong
Indonesia
Ireland
Italy
Japan
Mexico
New Zealand
Poland
South Korea
Spain
Sweden
Switzerland
Taiwan
United States
NOTE: The table identifies the 22 monetary authorities that
responded to the survey.
SURVEY RESULTS
To investigate the practice of foreign exchange
intervention, questionnaires on the topic were sent
to the 43 institutions that participated in the Bank
for International Settlements (BIS) (1999) survey of
foreign exchange practices and the European Central Bank. Of those 44 authorities, 22 responded to
some or all of the questions asked. Table 1 lists the
surveyed authorities who responded. A cover letter
explained that the survey covered practices over
1990-2000, so that authorities that no longer intervene—or even no longer have independent currencies—could report on past practices. The Reserve
Bank of New Zealand was the only authority to report that it had not intervened in the last 10 years.6
To respect the confidentiality of the respondents,
the responses of specific institutions will not be
identified unless the information is clearly in the
public domain. Table 2 shows statistics summarizing the distribution of the responses to survey
questions on the mechanics of, motivation for, and
the efficacy of intervention.
The Mechanics of Intervention
Question 1 inquires about the frequency of
intervention. Of the 14 authorities that responded
to the question, the percentage of business days
on which they report intervening—using either
sterilized or unsterilized transactions—ranged
from 0.5 percent to 40 percent, with 4.5 percent
being the median. While there might be some
selection bias because authorities that intervene
are more likely to respond to the survey, it does
4
M AY / J U N E 2 0 0 1
appear that official intervention is reasonably
common in foreign exchange markets.
In responding to question 2, 30 percent of authorities report that all their foreign exchange
transactions change the monetary base, 30 percent
that such dealings sometimes change the base, and
40 percent that they never change the base. For
some issues, such as motivation or time horizon of
effectiveness, this conflation of responses about
sterilized and unsterilized intervention is potentially unfortunate.
When monetary authorities do intervene
(question 3), they seem to have some preference
for dealing with major domestic banks but will also
transact with major foreign banks. This should not
come as a complete surprise as banks tend to specialize in trading their own national currencies
(Melvin, 1997). Approximately half of authorities
will sometimes conduct business with other entities, such as other central banks (23.5 percent) or
investment banks (25 percent); 6.3 percent will always transact with investment banks.
Intervention transactions over the last decade
have almost always been conducted at least partially in spot markets according to the answers to
question 4: 95.2 percent of authorities report that
their official intervention activities always include
spot market transactions and another 4.8 percent
sometimes include spot transactions. A total of
52.9 percent of authorities report sometimes using
the forward market, perhaps in conjunction with
the spot market to create a swap transaction. No
authority reports always using the forward market.
Finally, one authority reports having used a futures
market to conduct intervention.
There is no clear pattern as to the method of
dealing with counterparties (question 5). Direct
dealing over the telephone is most popular, being
used sometimes or always by 100 percent of authorities. Direct dealing over an electronic network
is used by 43.8 percent of authorities sometimes or
always. Live foreign exchange brokers are used
sometimes or always by 63.2 percent of the respondents. Finally, electronic brokers such as EBS
are used by 12.5 percent of the authorities. There
6
The Reserve Bank of New Zealand’s response on this point is a matter of public record. See Deputy Governor Sherwin’s May 9, 2000,
speech to the World Bank Treasury at
<http://www.rbnz.govt.nz/speeches/0092115.html>. This link was
current as of April 20, 2001. The Appendix to this article provides
links to descriptions of foreign exchange markets and/or intervention policies in the Web pages of a number of monetary authorities.
FEDERAL RESERVE BANK OF ST. LOUIS
Table 2
Summary of Intervention Survey Responses
1. In the last decade, on approximately what percentage of business
days has your monetary authority conducted intervention?
No. of
responses Minimum
Median Maximum
4.5
40.0
14
0.5
No. of
responses
Never
2. Foreign exchange intervention changes the domestic monetary base.
20
40.0
30.0
30.0
3. Intervention transactions are conducted with the following
counterparties:
Major domestic banks
Major foreign banks
Other central banks
Investment banks
21
18
17
16
0.0
16.7
76.5
68.8
28.6
72.2
23.5
25.0
71.4
11.1
0.0
6.3
4. Intervention transactions are conducted in the following markets:
Spot
Forward
Future
Other (please specify in margin)
21
17
16
15
0.0
47.1
93.8
93.3
4.8
52.9
6.3
6.7
95.2
0.0
0.0
0.0
5. Intervention transactions are conducted by:
Direct dealing with counterparties via telephone
Direct dealing with counterparties via electronic communication
Live FX brokers
Electronic brokers (e.g., EBS, Reuters 2002)
20
16
19
16
0.0
56.3
36.8
87.5
30.0
31.3
52.6
0.0
70.0
12.5
10.5
12.5
6. The following strategies determine the amount of intervention:
A prespecified amount is traded
Intervention size depends on market reaction to initial trades
17
20
17.6
5.0
70.6
55.0
11.8
40.0
7. Intervention is conducted at the following times of day:
Prior to normal business hours
Morning of the business day
Afternoon of the business day
After normal business hours
16
21
20
17
56.3
0.0
0.0
35.3
43.8
85.7
90.0
64.7
0.0
14.3
10.0
0.0
8. Is intervention sometimes conducted through indirect methods,
such as changing the regulations regarding foreign exchange
exposure of banks?
21
76.2
23.8
0.0
19
10.5
42.1
47.4
18
17
16
33.3
100.0
62.5
44.4
0.0
25.0
22.2
0.0
12.5
10. Intervention transactions are conducted secretly for the following
reasons:
To maximize market impact
To minimize market impact
For portfolio adjustment
Other
17
14
11
12
23.5
42.9
100.0
75.0
35.3
57.1
0.0
16.7
41.2
0.0
0.0
8.3
11. In your opinion, how long does it take to observe the full effect of
intervention on exchange rates?
A few minutes
A few hours
One day
A few days
More than a few days
Intervention has no effect on exchange rates
18
18
18
18
18
18
38.9
22.2
0.0
27.8
11.1
0.0
9. The following are factors in intervention decisions:
To resist short-term trends in exchange rates
To correct long-run misalignments of exchange rates from
fundamental values
To profit from speculative trades
Other
Sometimes Always
NOTE: Question 1 shows the minimum, median, and maximum responses (from 0 to 100) on the percentage of days intervention was
conducted in the last decade. Questions 2 through 10 show the percentage of responses of “Never,”“Sometimes,” and “Always” to those
questions. Question 11 shows the percentage of responses indicating that the full effects of intervention were felt at each horizon.
M AY / J U N E 2 0 0 1
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was some evidence that responding institutions are
moving increasingly toward electronic methods,
along with other market participants.
There has been very little research on factors
that determine the magnitude of intervention—
though reaction function research touches on this
issue—but the responses to question 6 indicate
that the size of interventions frequently depends
on market reaction to initial trades. Ninety-five
percent of monetary authorities report that market reaction sometimes or always affects the size
of total trades. Because the size of the intervention
is endogenous to market reaction, determining
the interaction of intervention and exchange rate
changes at high frequencies might require careful
evaluation.7
Most intervention takes place during the business day, but almost half the banks report that
they sometimes intervene prior to business hours
and more than half intervene after business
hours.8 For example, the Reserve Bank of
Australia publicly states that it is willing to intervene outside of normal business hours (Rankin,
1998). Some after-hours intervention might be in
support of other authorities. About a quarter of
central banks report that they always intervene in
the business morning (14.3 percent) or business
afternoon (10 percent), however.
In answering question 8, 23.8 percent of
authorities report sometimes intervening with
indirect methods. The authorities cite changing
banking regulations on foreign exchange exposure and moral suasion as methods of indirect
intervention. Not surprisingly, indirect methods
seem to be used predominantly by central banks
without a long history of free capital movements
or a convertible currency.
The Motivation for Intervention
The motivation for intervention decisions has
been widely researched and often discussed.
Research and official pronouncements support
the idea that monetary authorities with floating
exchange rates most often employ intervention
to resist short-run trends in exchange rates, the
leaning-against-the-wind hypothesis.9 Another
popular hypothesis is that intervention is used to
correct medium-term “misalignments” of exchange
rates away from “fundamental values.” Question 9
inquires about these possibilities. The responses
generally support these hypotheses with 89.5 percent of monetary authorities sometimes or always
6
M AY / J U N E 2 0 0 1
intervening to resist short-run trends and 66.7 percent seeking to return exchange rates to “fundamental values.” Some countries specified “other”
reasons that might be interpreted as variations on
the leaning-against-the-wind or misalignment
hypotheses. Still other countries note macroeconomic goals such as limiting exchange rate passthrough to prices, defense of an exchange rate
target, or accumulating reserves as motivating
intervention.
One hypothesis that has received some attention in the last few years is that profitability is a
consideration in intervention. A series of papers
have examined the profitability of intervention
(Leahy, 1995; Sweeney, 1997; Saacke, 1999), the
relationship between intervention profitability and
technical analysis (Neely, 1998, 2000), and
whether past profits influence intervention (Kim
and Sheen, 1999). While the early evidence
(Taylor, 1982b) indicated that central banks were
losing money on their intervention, the later
papers have been much more supportive of the
hypothesis that central banks have at least broken
even on floating rate intervention, with some evidence that they have made large profits.10
The notion that profitability is a consideration
in intervention decisions is uniformly rejected,
however, by the survey respondents. Not one
respondent to question 9 reports that profitability
was ever a motivation for intervention. Despite
this, private conversations with individuals
involved in intervention decisions suggest that
profitability is a useful gauge of their success as
careful stewards of public resources. In addition,
the Reserve Bank of Australia argues that profitability attests that its intervention has stabilized
the exchange rate (Rankin, 1998). This RBA claim
relies on Friedman’s (1953) claim that stabilizing
speculation is equivalent to profitable speculation.
If speculators consistently buy (sell) when the
asset price is below (above) its equilibrium value,
they will both tend to drive the asset price toward
7
The author thanks Lucio Sarno for pointing this out.
8
Fischer and Zurlinden (1999) examine the affect of intervention
using high frequency data and the time of data.
9
Indeed, the published statements of several central banks specifically cite the desire to counter trends in exchange rates as motivating intervention. See Board of Governors (1994, p. 64) or Rankin
(1998).
10
Sweeney (1997, 2000) argues that risk adjustment is crucial in
assessing profits or losses from official intervention.
FEDERAL RESERVE BANK OF ST. LOUIS
its equilibrium value and to profit from these
transactions.11 The link between profitability and
stabilizing speculation is tenuous, however. Salant
(1974), Mayer and Taguchi (1983), and De Long,
Schleifer, Summers, and Waldmann (1990) provide counterexamples.
The Role of Secrecy in Intervention
The role of secrecy in intervention is not well
understood. Most monetary authorities usually
chose to intervene secretly, releasing actual intervention data with a lag, if at all. Some authorities,
the Swiss National Bank, for example, always
publicize interventions at the time they occur.
Does secret intervention maximize or minimize
the impact of the transaction? If authorities intervene to convey information to markets, why do
they conceal these transactions? Dominguez and
Frankel (1993) recount several possibilities: When
fundamentals are inconsistent with intervention,
monetary authorities would prefer not to draw
attention to the intervention. Or, the monetary
authority might have poor credibility for sending
trustworthy signals. Or, the monetary authority
might wish to simply adjust the currency holdings
of its portfolio. Bhattacharya and Weller (1997)
provide another possible explanation for secrecy.
They present a model in which small amounts of
intervention reveal the authority’s information to
private parties, thus influencing exchange rates.
This secrecy issue has not been satisfactorily
resolved in the literature.
Consistent with the confusion in the academic
literature, the answers to question 10 reflect disagreement among the respondents about the purpose of secrecy. More authorities report sometimes
or always intervening secretly to maximize market impact (76.5 percent) than report sometimes
or always intervening secretly to minimize market
impact (57.1 percent). Such disagreement is significant. No central bank cites portfolio adjustment
as a reason for secret intervention, contrary to the
reasonable conjecture of Dominguez and Frankel
(1993). Of course, the central banks might not
consider transactions for portfolio adjustment to
be intervention.
ing the exchange rate. For many years, the biggest
hurdle to answering this question was the paucity
of data. More recently, even as more data have
become available, it is manifest that two barriers
to answering the question remain. First, what
would the exchange rate have been in the absence
of intervention? Second, over what horizon should
we measure the effectiveness of intervention and
how large and long-lasting an effect can be considered a success?
The academic literature has been ambivalent
about the efficacy of official intervention in the
foreign exchange market. The Jurgensen Report
(Jurgensen, 1983) was pessimistic about the effects
of intervention. Dominguez and Frankel (1993)—
using then-recently released intervention data—
argued that intervention can work by changing
expectations of future exchange rates. Edison
(1993) concluded that, while the evidence might
be consistent with some short-run effect, there is
no evidence for a lasting effect from intervention.
Sarno and Taylor (2000), in contrast, conclude
that the recent consensus of the profession is that
intervention is effective through both the signaling and portfolio balance channels.
Despite skepticism on the part of academics,
central banks continue to intervene—though perhaps less frequently than in the past—implying
that policymakers do think that intervention can be
an effective tool. Question 11 asks the monetary
authorities whether intervention has an effect on
exchange rates and, if so, over what horizon one
might see the full effect. All of the respondents indicate that they think intervention has some effect
on exchange rates.12 Most of the respondents
believe in a relatively rapid response, over a few
minutes (38.9 percent) or a few hours (22.2 percent). Still, a substantial minority think that intervention’s full effect is seen over a few days (27.8
percent) or more (11.1 percent). The dispersion in
the survey is substantial, indicating almost as much
discord among central bankers as among academics.
DISCUSSION AND CONCLUSION
This article has examined the mechanics of
intervention—instruments, counterparties, timing,
The Horizon of Intervention Effects
11
Perhaps the most important question in the
literature on central bank intervention is whether
central bank intervention is effective in influenc-
Friedman (1953) was referring more generally to speculation in foreign exchange and discussed government speculation (intervention)
as a special case.
12
Of course, having an effect on exchange rates at some horizon
might not imply that intervention is successful.
M AY / J U N E 2 0 0 1
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and amounts—as well as related issues like secrecy, motivation, and the perceived efficacy of such
transactions. A survey of monetary authorities’
intervention practices reveals that a number of
monetary authorities do intervene with some frequency in foreign exchange (mostly spot) markets.
The desire to check short-run trends or correct
longer-term misalignments often motivates intervention, whereas the size of intervention often
depends on market reaction to initial trades.
Although intervention typically takes place during
business hours, most monetary authorities will
also intervene outside of these hours, if necessary.
And, while there is unanimous agreement that
intervention does influence exchange rates, there
is much disagreement about the horizon over
which the full effect of this influence is felt, with
estimates ranging from a few minutes to more
than a few days.
The topic of the practice of official intervention is very broad. To simplify this study, issues
like coordinated versus unilateral intervention,
choice of intervention currency, and distinguishing between intervention in fixed and flexible
exchange rate regimes have been left for further
study. Other issues that merit further consideration are motivations for secrecy and the metric for
judging the success of intervention.
REFERENCES
Bank for International Settlements. “Central Bank Survey
of Foreign Exchange and Derivatives Market Activity.”
May 1999.
Bhattacharya, Utpal and Weller, Paul A. “The Advantage to
Hiding One’s Hand: Speculation and Central Bank
Intervention in the Foreign Exchange Market.” Journal of
Monetary Economics, July 1997, 39(2), pp. 257-77.
Dominguez, Kathryn and Frankel, Jeffery. “Does Foreign
Exchange Intervention Work?” Washington, DC: Institute
for International Economics, 1993.
Dooley, Michael P.; Mathieson, Donald J. and Rojas-Suarez,
Liliana. “Capital Mobility and Exchange Market
Intervention in Developing Countries.” Working Paper
No. 6247, National Bureau of Economic Research
(Cambridge, MA), October 1997.
Economist. “Spanish Peseta; Adios.” 1 May 1993, pp. 84.
Edison, Hali J. “The Effectiveness of Central-Bank
Intervention: A Survey of the Literature After 1982.”
Special Papers in International Economics No. 18,
Department of Economics, Princeton University, 1993.
Financial Times. “Peseta Devalued by 8% in ERM: Lisbon
Realigns Escudo as Madrid Plans 1 1/2 Point Rate Cut.”
14 May 1993, pp. 1.
Fischer, Andreas M., and Zurlinden, Mathias. “Exchange
Rate Effects of Central Bank Interventions: An Analysis
of Transaction Prices.” Economic Journal, October 1999,
109(458), pp. 662-76.
Friedman, Milton. Essays in Positive Economics. Chicago:
University of Chicago Press, 1953, pp. 157-203.
Galan Medina, Manuel; Duclaud Gonzalez de Castilla, Javier
and Garcia Tames, Alonso. “A Strategy for Accumulating
Reserves Through Options to Sell Dollars.” Unpublished
manuscript, Banco de Mexico, 1997. <http://www.
banxico.org.mx/siteBanxicoINGLES/bPoliticaMonetaria/
FSpoliticaMonetaria.html>.
Jurgensen, Philippe. “Report of the Working Group on
Exchange Market Intervention.” Washington, DC: U.S.
Department of the Treasury, March 1983.
Blejer, Mario I. and Schumacher, Liliana. “Central Banks
Use of Derivatives and Other Contingent Liabilities:
Analytical Issues and Policy Implications.” Working
Paper No. 00-66, International Monetary Fund
(Washington, DC), March 2000.
Kim, Suk-Joong and Sheen, Jeffery. “The Determinants of
Foreign Exchange Intervention by Central Banks:
Evidence from Australia.” Unpublished manuscript, The
University of New South Wales, December 1999.
Board of Governors of the Federal Reserve System. The
Federal Reserve System: Purposes and Functions.
Washington, DC, 1994.
Leahy, Michael P. “The Profitability of US Intervention in
the Foreign Exchange Markets.” Journal of International
Money and Finance, December 1995, 14(6), pp. 823-44.
De Long, J. Bradford; Shleifer, Andrei; Summers, Laurence
H. and Waldmann, Robert J. “Noise Trader Risk in
Financial Markets.” Journal of Political Economy, August
1990, pp. 703-38.
Mayer, Helmut and Taguchi, Hiroo. “Official Intervention
in the Exchange Markets: Stabilising or Destabilising?”
Bank for International Settlements Economic Paper 6,
March 1983.
8
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FEDERAL RESERVE BANK OF ST. LOUIS
Melvin, Michael T. International Money and Finance. 5th
Ed. Reading, MA: Addison Wesley, 1997.
___________. “Official Intervention in the Foreign Exchange
Market, or, Bet Against the Central Bank.” Journal of
Political Economy, April 1982b, 90(2), pp. 356-68.
Moreno, Ramon. “Lessons from Thailand.” Federal Reserve
Bank of San Francisco Economic Letter No. 97-33, 7
November 1997.
Neely, Christopher J. “Technical Analysis and the
Profitability of U.S. Foreign Exchange Intervention.”
Federal Reserve Bank of St. Louis Review, July/August
1998, 80(4), pp. 3-17.
___________. “An Introduction to Capital Controls.” Federal
Reserve Bank of St. Louis Review, November/December
1999, 81(6), pp. 13-30.
___________. “The Temporal Pattern of Trading Rule
Returns and Central Bank Intervention: Intervention
Does Not Generate Trading Rule Profits.” Working Paper
2000-18B, Federal Reserve Bank of St. Louis, August 2000.
Rankin, Bob. “The Exchange Rate and the Reserve Bank’s
Role in the Foreign Exchange Market.” Reserve Bank of
Australia, 1998. <http://www.rba.gov.au/publ/pu_teach_
98_2.html>.
Saacke, Peter. “Technical Analysis and the Effectiveness of
Central Bank Intervention.” Unpublished manuscript,
University of Hamburg, 1999.
Salant, Stephen W. “Profitable Speculation, Price Stability,
and Welfare.” International Finance Discussion Paper
54, Board of Governors of the Federal Reserve System,
November 1974.
Sarno, Lucio and Taylor, Mark P. “Official Intervention in
the Foreign Exchange Market.” Unpublished manuscript,
University College, Oxford, 2000.
Sweeney, Richard J. “Do Central Banks Lose on ForeignExchange Intervention? A Review Article.” Journal of
Banking and Finance, December 1997, 21(11-12), pp.
1667-84.
___________. “Does the Fed Beat the Foreign Exchange
Market?” Journal of Banking and Finance, May 2000,
24(5), pp. 665-94.
Taylor, Dean. “The Mismanaged Float: Official Intervention
by the Industrialized Countries,” in Michael B. Connolly,
ed., The International Monetary System: Choices for the
Future. Westport, CT: Praeger Publishers, 1982a, pp. 4984.
M AY / J U N E 2 0 0 1
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Appendix
Monetary Authority Web Pages Describing Intervention or Foreign Exchange Markets
Monetary authority
Uniform Resource Locator on intervention or
foreign exchange
Notes on the pages
Reserve Bank of
Australia
http://www.rba.gov.au/publ/pu_teach_98_2.html
Central Bank of Brazil
http://www.bcb.gov.br/ingles/himf1900.shtm#destino6 Foreign exchange policy.
Bank of Canada
http://www.bankofcanada.ca/en/backgrounders/bge2.htm
Concise description of intervention policy.
Bank of England
http://www.bankofengland.co.uk/factfxmkt.pdf
Description of foreign exchange
markets.
European Central Bank
http://www.ecb.int
No work directly describing intervention policy but speeches and
working papers may relate.
German Bundesbank
http://www.bundesbank.de/en/presse/
pressenotizen/pdf/mp100197.pdf
1997 description of the ECB’s
responsibility in foreign exchange
intervention.
Hong Kong
Monetary Authority
http://www.info.gov.hk/hkma/eng/speeches/speechs/
joseph/speech_030398b.htm
Speech by the CEO describing
exchange rate policy.
Bank of Japan
http://www.boj.or.jp/en/faq/faqkainy.htm
Outline of the Bank of Japan’s foreign exchange intervention
operations.
Banco de Mexico
http://www.banxico.org.mx/siteBanxicoINGLES/
bPoliticaMonetaria/FSpoliticaMonetaria.html
Description of monetary and
exchange rate policies.
National Bank of Poland
http://www.nbp.pl/en/publikacje/index.html
Some descriptions of intervention
in annual reports.
Monetary Authority
of Singapore
http://www.mas.gov.sg/resource/index.html
See the pamphlet, Economics
Explorer #1.
Reserve Bank of
South Africa
http://www.resbank.co.za/ibd/fwdcover.html
Describes South Africa’s use of
forward cover in somewhat technical terms.
Swiss National Bank
http://www.snb.ch/e/publikationen/publi.html
Some intervention descriptions in
annual reports.
Bank of Thailand
http://www.bot.or.th/BOTHomepage/BankAtWork/
Monetary&FXPolicies/EXPolicy/8-23-2000-Eng-i/
exchange_e.htm
Very brief description of current
exchange rate policy.
Federal Reserve of the
United States
http://www.federalreserve.gov/pf/pdf/frspurp.pdf
See page 64 for a very brief
description of intervention policy.
10
M AY / J U N E 2 0 0 1
Excellent descriptive page on markets and intervention.
FEDERAL RESERVE BANK OF ST. LOUIS
Forecasting Inflation
and Growth: Do Private
Forecasts Match Those
of Policymakers?
William T. Gavin and Rachel J. Mandal
enerally, we value forecasts for their accuracy. In some cases, however, the forecasts
themselves are interesting because of
what they reveal about the forecaster. Monetary
policymaker forecasts are important because they
partially reveal what policymakers believe will follow from their decisions.
Forecasts of inflation and real output (whether
made by Federal Reserve officials or private sector
economists) contain information that is important
for changing the stance of monetary policy. Market
participants generally believe that Fed policymakers
will change their policy stance if the economy
appears to be headed in a different direction from
what was expected at the time policy was adopted.
Svensson (1997) and Svensson and Woodford (2000)
explain why a central bank might want to target its
inflation forecast. The intuition in their explanation
is that policymakers should look at everything that
is relevant when deciding to change the policy
stance. The trouble with looking at everything is
that there is so much information to process, one
needs an organizing framework such as a forecasting model. Forecasting models are developed
to monitor incoming information and to weigh
each piece appropriately. Forecasting models
range from the very largest, with over a thousand
equations, to small models that are no more than
simple rules of thumb. Whether using a large
econometric model or a simple rule of thumb,
forecasters rarely use the values that come directly
from the model. Rather, they typically make judgmental adjustments before reporting the forecasts.
In this article, we examine the role of forecasts
G
William T. Gavin is a vice president and senior economist and
Rachel Mandal is a research associate at the Federal Reserve Bank
of St. Louis. The authors thank Dean Croushore and Dinah Maclean
for helpful comments. The original version of this article, winner of
the Edmund A. Mennis Contributed Paper Award for 2000, appeared
in the January 2001 issue of Business Economics, the journal of the
National Association for Business Economics (NABE). For more
information, visit NABE’s Web site at <www.nabe.com>.
in the monetary policy process. Our focus is on
the forecasts of inflation and economic growth,
the main policy objectives. Economic forecasts are
important because they reflect incoming information about the current state of the economy,
including the forecasters’ beliefs about monetary
policy objectives. In the United States, there are no
explicit numerical objectives for output and inflation. Thus, policymaker forecasts are particularly
interesting because they may reveal information
about long-run policy goals.
Fed forecasts, unfortunately, are not readily
available to the public. We show that the Blue Chip
consensus forecasts, made by a group of private
economists, are a good stand-in for the policymakers’ forecasts. This is important because the
policymakers in the Federal Reserve, the members
of the Federal Open Market Committee (FOMC),
reveal their forecasts only sparingly and after policy
decisions are made. First, we show how well the
forecasts match. We find that the forecasts of economic growth are very similar and appear to be
about equal on average. The result for inflation
forecasts is more interesting. Here we see that the
private sector economists generally predicted
higher inflation than did Fed policymakers,
especially in the 1980s. The Blue Chip economists
did not believe that the FOMC would achieve and
maintain such a low inflation rate in the 1980s.
Since 1995, the forecasts have converged. Evidently, the FOMC has achieved some credibility
with the Blue Chip economists.
When researchers want to know the history of
policymakers’ forecasts, they typically go to the
Fed’s briefing documents to extract the forecasts
of the research staff at the Board of Governors. We
show that the Blue Chip forecasts for output are as
good a proxy for Fed policymakers’ views as are
the research staff forecasts. In the case of inflation,
the results vary with the time horizon. Generally,
the Blue Chip consensus forecasts for inflation
match the policymakers’ forecasts at shorter horizons while the research staff forecasts are closer at
the longest horizon.
Finally, we examine the use of alternative forecasts in a version of the Taylor rule, a popular
characterization of monetary policy actions. It is
popular because it is a simple summary of a complicated policy process. It is expressed as:
(1) FFtA = r e + p t -1 + 0.5 (p t -1 - p T ) + 0.5 ( yt -1 - ytF-1 ) ,
where FFtA is the federal funds rate target chosen
M AY / J U N E 2 0 0 1
11
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by the FOMC, r e is the long-run equilibrium real
interest rate (assumed by Taylor to be equal to 2
percent per year), pt–1 is the average inflation rate
observed over the previous four quarters, p T is the
inflation target (which Taylor assumed to be equal
to 2 percent per year), yt–1 is last period’s real
gross domestic product (GDP) measured in
logarithms, and ytF–1 is last period’s potential real
GDP measured in logarithms. The term in the
bracket, ( yt–1 – ytF–1), is approximately equal to the
percentage deviation of GDP from the perceived
level of potential GDP.
This backward-looking rule prescribes settings
for the federal funds rate, the Fed’s short-term
policy instrument, according to the deviation of
the past year’s inflation from a 2 percent target
and the deviation of last period’s GDP from a measure of potential GDP. We begin by showing that
historical analysis of the Taylor rule should use
real-time data; that is, data that were available
when the federal funds rate target was being set.
We show that the forward-looking rule based on
policymaker forecasts is virtually identical to one
based on Blue Chip consensus forecasts. Neither
does quite as well as the backward-looking rule
using real-time data; however, all three versions of
the Taylor rule do much better at explaining
historical movement in the federal funds rate than
do rules based on the current revised data.
Because purely forward-looking rules may be
inherently unstable, we also examine a combination rule that includes both lagged values of
inflation and the output gap using real-time data
and the Blue Chip forecasts of the current-year
inflation and output gap.1 This rule with both
backward- and forward-looking elements matches
the actual federal funds rate slightly better than
the rule based on real-time data.
FOMC AND BLUE CHIP FORECASTS
FOMC members prepare forecasts for Congressional testimony twice a year.2 This testimony was
mandated by the Full Employment and Balanced
Growth Act of 1978. Section 108 of this act explicitly required the Fed to submit “written reports
setting forth (1) a review and analysis of recent
developments affecting economic trends in the
nation; (2) the objectives and plans … with respect
to the monetary and credit aggregates …; and (3)
the relationship of the aforesaid objectives and
plans to the short-term goals set forth in the most
12
M AY / J U N E 2 0 0 1
recent Economic Report of the President …” In
order to satisfy the third item, the Federal Reserve
Chairman began reporting a summary of Fed policymakers’ forecasts to Congress in July 1979. Since
then, similar summaries of forecasts have been
reported every February and July.3 Forecasts are
made of annual, fourth-quarter-over-fourthquarter growth rates for nominal GDP, real GDP,
and inflation.4 Fed policymakers also forecast the
average level of unemployment for the fourth
quarter of the year. In February, the forecasts pertain to the current calendar year (referred to below
as the 12-month-ahead forecast). In July, forecasts
are updated for the current calendar year (6-monthahead forecasts) and preliminary projections are
made for the next calendar year (18-month-ahead
forecasts).
We focus on the forecasts of real output growth
and inflation because they best capture monetary
policy objectives. We use the output price deflator
as the measure of inflation primarily because it
has been consistently forecasted throughout the
entire period. Even when the Fed was reporting
the forecast for inflation based on the consumer
price index (from 1989 through 1999), there was
also a forecast for both nominal and real output,
so there was always an implied forecast for the
output deflator.
Individual Federal Reserve officials submit
their economic forecasts based on their judgment
about the appropriate policy to be followed over
the coming year. These individual projections may
be revised after the FOMC adopts a specific policy.
The revised projections are then reported as a
range, listing the high and low values for each item,
and as a central tendency that omits extreme forecasts and is meant to be a better representation of
1
See Woodford (2000) for a summary of the argument that purely
forward-looking rules may lead to instability.
2
The FOMC is the policymaking committee of the Federal Reserve
System. When the Board is full, the Committee consists of the 7
governors of the Board, the president of the Federal Reserve Bank
of New York, and 4 of the remaining 11 Federal Reserve Bank presidents who serve on a rotating basis. All 12 presidents attend every
meeting, contribute to the discussion, and provide forecasts that
are summarized in testimony to the Congress. The Green Book is a
briefing document with macroeconomic forecasts prepared by staff
economists at the Board of Governors about three workdays before
each FOMC meeting.
3
This reporting requirement has now expired, but the Fed provided
forecasts to Congress on July 20, 2000, and February 13, 2001.
These data are not included in this study.
4
The Fed switched from GNP to GDP in 1992.
FEDERAL RESERVE BANK OF ST. LOUIS
Figure 1
Figure 2
Output Forecasts (1983 to 1994)
Inflation Forecasts (1983 to 1994)
7
6
45 line
6
5
Blue Chip
5
Green Book
Blue Chip
Green Book
4
4
3
3
2
2
1
1
0
45 line
0
0
1
2
3
4
5
Midpoint of FOMC Central Tendency
6
7
the consensus view. In this article, we define the
consensus FOMC forecast as the midpoint of this
central tendency range.
The Blue Chip consensus forecasts are taken
from the February and July reports. These forecasts
are collected on the first three working days of the
month, and the information available to private
sector economists is approximately the same as
the information available to the FOMC members
when they make their forecasts. Most importantly,
both groups usually had the latest information on
the price indexes from the Bureau of Labor Statistics and the most recent report on actual GDP
from the Bureau of Economic Analysis.
Figure 1 is a scatter diagram with triangles
showing the relation between the consensus GDP
growth forecasts made by the FOMC between
1983 and 1994 and those made by the Blue Chip
economists during the same period. We start in
1983 because that is when the Federal Reserve
first began to report the central tendency of the
forecasts. It was also the first year that they
reported forecasts for all the participants: FOMC
members and nonvoting Federal Reserve Bank
presidents.5
If the FOMC and Blue Chip forecasts were
exactly the same, they would lie on the 45-degree
line shown. As Figure 1 shows, the forecasts were
quite similar and seem to be distributed evenly
above and below the 45-degree line. That is, there
0
1
2
3
4
Midpoint of FOMC Central Tendency
5
6
does not seem to be any tendency for the Blue
Chip economists to systematically forecast more
or less output growth than the FOMC.
The same cannot be said of the inflation forecasts. The triangles in Figure 2, where most of the
points lie above the 45-degree line, show that the
Blue Chip economists usually forecasted higher
inflation than did the FOMC. The period from
1983 to the present has been a period of moderate
and falling inflation. Throughout, the Federal
Reserve has had a goal of eliminating inflation. In
general, the FOMC’s forecasts of inflation have
been lower than the Blue Chip forecasts. However,
as inflation became lower in the 1990s, the
forecasts have converged, indicating that the
private sector has gained confidence in the Fed’s
ability to deliver low inflation. So, although the
Blue Chip inflation forecasts have not always been
unbiased indicators of the FOMC’s inflation
forecasts, they have been better in recent years.
GREEN BOOK FORECASTS
The Green Book forecast is put together by a
large staff of economists at the Board of Governors
in Washington, D.C. It is prepared for the FOMC
members who read it in advance of the meetings
5
In July 1979, the Fed reported a range of Board member forecasts
(governors only). From 1980 through 1982, the Fed reported a
range of forecasts for FOMC members.
M AY / J U N E 2 0 0 1
13
REVIEW
Table 1
Blue Chip Versus Green Book as a Proxy for FOMC Forecasts (1983 to 1994)
RMSE of output forecast
All 3 horizons
RMSE of inflation forecast
Blue Chip
Green Book
Blue Chip
Green Book
0.22
0.36
0.32
0.38
6-Month horizon
0.17
0.35
0.21
0.25
12-Month horizon
0.25
0.32
0.32
0.38
18-Month horizon
0.24
0.40
0.40
0.47
NOTE: Bold typeface indicates a better proxy for the midpoint of the FOMC tendency.
and receive an oral presentation of this forecast at
the meeting. These forecasts are only available to
the public five years after they are made.
Romer and Romer (2000) compare the Green
Book forecasts with private sector forecasts using
quarterly data from 1965 through 1991 and forecasts over several horizons (usually from forecasts
of the current quarter out to seven quarters ahead).
They present convincing evidence that the Green
Book inflation forecasts have been more accurate
than the private forecasts, including the Blue Chip
consensus (for the period from 1980 to 1991).
They also report that the Green Book forecasts of
output were better than private sector forecasts,
but the evidence for output forecasts is weaker.
The Green Book forecasts from 1983 through
1994 are depicted as circles in Figures 1 and 2.
Casual observation suggests that the Green Book
forecasts and the Blue Chip consensus represent
the policymakers’ consensus equally well. These
scatter diagrams combine forecasts across the
three horizons of 6, 12, and 18 months ahead.
Table 1 gives more detailed information about
how well the Blue Chip consensus and the Green
Book forecast match the FOMC consensus. Results
are reported for the combined forecasts (combined
over the three forecasting horizons) and for the
three separate horizons. The forecast error in
Table 1 is defined as the difference between the
alternative forecast (Blue Chip consensus or Green
Book) and the midpoint of the FOMC central
tendency forecast. We report root-mean-squared
errors (RMSE) for both inflation and output
forecasts.
The results are interesting. On average, the differences in errors between the Green Book and
Blue Chip are larger for the real output forecasts
than they are for the inflation forecasts. For both
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M AY / J U N E 2 0 0 1
real output and inflation, the Blue Chip consensus
is closer to the FOMC forecast than is the Green
Book. For the first 12 years after the FOMC began
reporting the central tendency, the Blue Chip forecast has provided a good measure of the FOMC’s
view of the future, as least as good as one would
get by knowing the Green Book forecast.
RELATIVE ACCURACY
1983 Through 1994
Table 2 reports the relative accuracy of real
output forecasts to the real-time data from 1983
through 1994. For the separate and combined
horizons, we compare the individual forecasts to
the value that was first reported by the Bureau of
Economic Analysis.6 The Blue Chip forecasts are
best (lowest RMSE) for the 12- and 18-month
horizons. The FOMC’s forecasts have the lowest
RMSE at the 6-month horizon. In none of these
cases are the Green Book forecasts of real output
best.7
The Green Book fares better, however, for
inflation forecasts from 1983 through 1994, as
shown in Table 3. Earlier, we saw that the Blue
Chip inflation forecasts were generally above the
FOMC’s forecasts in the 1980s. Here we see that all
three forecasts, on average, predicted higher than
actual inflation, with the FOMC forecasts sandwiched between the Blue Chip forecasts on the
6
We used the vintage data sets from the Federal Reserve Bank of
Philadelphia described in Croushore and Stark (1999).
7
This is surprising given the conclusions in Romer and Romer (2000).
They examined an earlier and longer sample with more frequent
forecasts over more horizons. We examine only those dates and
forecast horizons for which the central tendency of FOMC members’
forecasts were reported to Congress.
FEDERAL RESERVE BANK OF ST. LOUIS
Table 2
Accuracy of Output Forecasts (1983 to 1994)
Mean error
RMSE
Blue Chip
FOMC members
Green Book
Blue Chip
FOMC members
Green Book
0.04
0.06
–0.06
0.94
0.96
1.05
All 3 horizons
6-Month horizon
0.02
0.05
–0.02
0.76
0.74
0.80
12-Month horizon
–0.11
–0.08
–0.15
1.05
1.11
1.23
18-Month horizon
0.22
0.22
–0.02
0.99
1.00
1.06
NOTE: Best forecast indicated by bold typeface.
Table 3
Accuracy of Inflation Forecasts (1983 to 1994)
Mean error
RMSE
Blue Chip
FOMC members
Green Book
Blue Chip
FOMC members
Green Book
All 3 horizons
0.69
0.46
0.35
0.92
0.80
0.65
6-Month horizon
0.45
0.33
0.21
0.64
0.55
0.36
12-Month horizon
0.60
0.41
0.26
0.79
0.74
0.61
18-Month horizon
1.01
0.65
0.57
1.23
1.05
0.88
NOTE: Best forecast indicated by bold typeface.
high end and the more accurate Green Book forecasts on the low end.
1995 Through 1999
Table 4 examines the accuracy of the Blue
Chip and FOMC real output forecasts from 1995
through 1999. Again, we report results based on
the combined data sets and also separately for
each forecast horizon. For these five years, both
the Blue Chip and the FOMC policymakers’ forecasts for real output growth were about 1 percent
below actual. The large bias in the mean error
reflects the ongoing surprise about the strength of
economic growth and upward revisions to
estimates of the underlying trend. We find that in
the last five years, on average, the FOMC has been
more accurate, as measured by the RMSE, than the
Blue Chip at all forecast horizons.
We saw in Figure 2 that the FOMC and Blue
Chip forecasts converged as inflation came down
in the 1990s. Table 5 looks at the accuracy of the
Blue Chip and FOMC inflation forecasts over the
last five years of the sample. Both the FOMC and
Blue Chip forecasts predicted higher than actual
inflation from 1995 through 1999. The FOMC
inflation forecasts have been slightly more
accurate than the Blue Chip forecast for all three
forecast horizons.
Although the FOMC forecasts were more accurate than the Blue Chip forecasts, the forecasts
were not far apart. On average for all three horizons,
the Blue Chip consensus for GDP growth was a
tenth of a percentage point below the FOMC’s, and
the Blue Chip consensus for inflation was onetenth higher than the FOMC’s. The five years
reported in Tables 4 and 5, 1995 through 1999,
have been characterized by surprisingly high real
GDP growth and surprisingly low inflation, as is
seen by the negative mean errors for output growth
and the positive mean errors for inflation.
USING FORECASTS IN TAYLOR-TYPE
RULES
In this section we use a simple policymaking
framework to see whether the differences between
the Blue Chip and FOMC forecasts are economically
M AY / J U N E 2 0 0 1
15
REVIEW
Table 4
Accuracy of Output Forecasts (1995 to 1999)
Mean error
RMSE
Blue Chip
FOMC members
Green Book
Blue Chip
FOMC members
Green Book
All 3 horizons
–1.13
–1.02
NA
1.46
1.35
NA
6-Month horizon
–0.52
–0.53
NA
0.81
0.73
NA
12-Month horizon
–1.26
–1.01
NA
1.67
1.50
NA
18-Month horizon
–1.73
–1.65
NA
1.78
1.71
NA
Green Book
Blue Chip
FOMC members
NOTE: Best forecast indicated by bold typeface.
Table 5
Accuracy of Inflation Forecasts (1995 to 1999)
Mean error
Blue Chip
FOMC members
RMSE
Green Book
All 3 horizons
0.59
0.48
NA
0.72
0.64
NA
6-Month horizon
0.36
0.29
NA
0.43
0.39
NA
12-Month horizon
0.52
0.37
NA
0.64
0.50
NA
18-Month horizon
0.98
0.86
NA
1.03
0.96
NA
NOTE: Best forecast indicated by bold typeface.
significant. Taylor (1993) proposed characterizing
past Fed policy as if it were made according to a
formula similar to equation (1), which has come to
be known as the Taylor rule.8
Rotemberg and Woodford (1999) show that a
rule of this form can be derived as an optimal
policy under certain conditions. Clarida, Gali, and
Gertler (1999) show that a rule of this type can be
optimal in a dynamic, forward-looking IS/LM
model in which the central bank’s loss function is
quadratic in deviations of inflation from target and
output from potential. Even if the central bank
cares only about the inflation objective, the nominal interest rate target may be set as a function of
the state of the economy. If the real interest rate is
procyclical, adjusting the federal funds rate target
for changes in the gap between potential and
actual GDP may be a method for taking into
account the cyclical deviation of the real interest
rate from the long-run equilibrium value.9
While clearly not advocating that any central
bank follow any such simple rule slavishly, Taylor
recommended his rule as a reference point in
16
M AY / J U N E 2 0 0 1
debates about whether a policy change might be
needed. Indeed, that has happened as many
central banks now regularly monitor variations of
the original Taylor rule. Figure 3 shows the quarterly
average federal funds rate and our calculation of
the federal funds rate target implied by the Taylor
rule for the period from 1983 to 1999.
We begin by showing the federal funds rate
target implied by equation (1).10 As Figure 3 shows,
the rule does not do particularly well during the
periods before 1990 or after 1994. Table 6 shows
that the federal funds rate target, predicted by
using current revised data, is, on average, 166 basis
points below the actual federal funds rate.
8
In his 1993 paper, Taylor used current year values for the GDP gap
and inflation. Since current year data are unknown at the time policy is made, we have used lagged values.
9
For recent evidence suggesting that the real interest rate is procyclical, see Dotsey and Scholl (2000).
10
Note that the usefulness of the Taylor rule has been questioned by
many researchers, including recent articles by Hetzel (2000), Kozicki
(1999), McCallum (1999), and Orphanides (1998).
FEDERAL RESERVE BANK OF ST. LOUIS
Figure 3
Figure 4
Taylor Rules: Current Versus Real-Time Data
Taylor Rules: Blue Chip Versus FOMC
Forecasts (Semi-Annual Data)
12
12
Actual FF
Taylor Rule with
Real-Time Data
10
Blue Chip
10
Fed Policymakers
8
Actual Federal
Funds Rate
8
6
6
4
2
0
1983
4
Taylor Rule with
Current Vintage Data
2
1985
1987
1989
1991
1993
1995
1997
1999
Figure 3 also includes the Taylor rule for the
federal funds rate target using real-time data for
GDP and inflation and a forecast for potential GDP
from a recursive model that fits a quadratic time
trend to the real-time data. As the figure shows,
there is an important difference in the target
calculated for the federal funds rate when we use
the real-time data. Contrary to the case using currently available revised data, the real-time Taylor
rule generally lies above the actual federal funds
rate. The right-most column in Table 6 shows that
the average deviation was 34 basis points. These
results show that ex post policy rules based on
revised data may do a poor job of replicating
actual policy choices.
Figure 4 includes two versions of a forwardlooking Taylor rule where we modify Taylor’s
general specification by replacing the backwardlooking measures of inflation and output with
FOMC and Blue Chip forecasts for the calendar
year. The modified Taylor rule used is
(2)
FFtB = r e + p te + 0.5 (p te - p T ) + 0.5 ( yte - ytF ),
where pte is the forecast of fourth-quarter-overfourth-quarter inflation for the current year and
( yte – ytF ) is the output gap expected for the current
year. We use the real-time data and our quadratic
time trend to predict potential GDP in the fourth
quarter of each year. We construct a fourth-
0
1983
1985
1987
1989
1991
1993
1995
1997
1999
quarter forecast of the level of GDP using the
actual real-time value of the previous fourthquarter level of GDP and the fourth-quarter-overfourth-quarter forecast of GDP for the current year.
Whether we use forecasts from the FOMC or Blue
Chip, the implications for the federal funds rate
target are almost identical.
In Table 6, the RMSE between the actual
federal funds rate and the target predicted by the
alternative Taylor rules are given along a diagonal
in parentheses. For this period, using these forecasts, the backward-looking rule using real-time
data predicts the actual federal funds rate slightly
more accurately than do the forward-looking rules.
The forward-looking version using the Blue Chip
consensus forecasts is more accurate than the
version using FOMC forecasts. However, the mean
error for the FOMC version is closest to zero. As we
saw in Figure 4, the Blue Chip and FOMC versions
of the Taylor rule seem to move in tandem. The
correlation between these versions of the Taylor
rule is 0.99.
Bernanke and Woodford (1997) have argued
that purely forward-looking Taylor rules may not
be practical. Chari (1997) explains simply,
Suppose, for instance, that the central bank
wants to stabilize inflation rates and private
forecasters have information that is not
available to the central bank about future
M AY / J U N E 2 0 0 1
17
REVIEW
Table 6
Alternative Versions of the Taylor Rule
Actual
federal
funds rate
Current
revised
data
Current revised data
0.73
(1.42)
Real-time data
0.87
0.82
(1.04)
Real-time
data
Blue Chip
forecast
FOMC Combination:
members’ real time and
forecasts
Blue Chip
Mean
error
–1.66
0.34
Blue Chip forecast
0.84
0.67
0.92
(1.16)
FOMC members’
forecasts
0.82
0.67
0.91
0.99
(1.23)
0.15
Combination: real time
and Blue Chip
0.88
0.76
0.98
0.98
0.97
–0.03
(1.02)
0.24
NOTE: Correlations among the alternative predictions of the Taylor rule and the actual federal funds rate are shown in bold. RMSE
are shown in parentheses (Taylor rule minus actual federal funds rate). Right column shows the mean error for each version of the
Taylor rule.
inflation. The central bank could use private forecasts of inflation to choose its
policy instrument. The problem is that if
the central bank is completely effective in
using its policy instrument to stabilize inflation, private forecasts of inflation should
rationally be the central bank’s inflation
target in which case, private forecasts provide no information about inflation! This
paradox arises because market forecasts
of a goal variable depend upon the central
bank’s policy rule and if the central bank
used the information well, market forecasts will not be informative. (p. 685)
Woodford (2000) recommends policies that
include both backward- and forward-looking
elements. We create a combination rule that uses
both the lagged values of inflation and the output
gap as well as the Blue Chip forecasts for the current year. It is equivalent to taking an average of the
real-time Taylor rule (FFtA ) and the forward-looking
rule using Blue Chip forecasts (FFtB ). The results for
this combination rule are given in the bottom row
of Table 6. The federal funds rate target that comes
out of this rule has the highest correlation with
the actual federal funds rate (0.88) and the lowest
RMSE (1.02) of all the rules that we considered.
CONCLUSION
We have found that the Blue Chip consensus
appears to have been closely matched to the mid18
M AY / J U N E 2 0 0 1
point of the FOMC’s central tendency forecasts.
During the period from the beginning of 1983
through the summer of 1994, the Blue Chip
forecasts for output were not only more closely
related to the FOMC’s output forecasts, but they
were slightly more accurate than the forecasts in
the Green Book. The Green Book forecasts of inflation were much more accurate than were the Blue
Chip’s during the period between 1983 and 1994.
Nevertheless, the Blue Chip forecasts were still as
closely related to the FOMC forecasts as were the
Green Book forecasts.
In the period since 1994, the FOMC consensus has been more accurate than the Blue Chip
consensus for both inflation and output, but not
by much. During the period from 1995 through
1999, inflation has been lower than expectations
while the real economy has been unexpectedly
strong.
For the entire period, the differences between
the Blue Chip consensus forecasts and the midpoint of the central tendency are not statistically
or economically relevant for the policymaking
process, at least not as that process has been
characterized by Taylor (1993). We should not be
surprised to learn that the Blue Chip forecasts of
inflation and output are highly correlated with
FOMC forecasts. Both the FOMC members and
the economists who contribute to the Blue Chip
consensus observe the same statistical releases
and use similar economic theories to interpret
the data.
FEDERAL RESERVE BANK OF ST. LOUIS
REFERENCES
Bernanke, Ben S. and Woodford, Michael. “Inflation
Forecasts and Monetary Policy.” Journal of Money, Credit
and Banking, November 1997, 29(4, Part II), pp. 653-84.
Chari, V.V. “Comment on Inflation Forecasts and Monetary
Policy.” Journal of Money, Credit and Banking, November
1997, 29(4, Part II), pp. 685-86.
Clarida, Richard; Gali, Jordi and Gertler, Mark. “The
Science of Monetary Policy: A New Keynesian
Perspective.” Journal of Economic Literature, December
1999, 37(4), pp. 1661-707.
Croushore, Dean and Stark, Tom. “A Real-Time Data Set for
Macroeconomists.” Working Paper 99-4, Federal Reserve
Bank of Philadelphia, June 1999.
Dotsey, Michael and Scholl, Brian. “The Behavior of the
Real Rate of Interest Over the Business Cycle.”
Unpublished manuscript, Federal Reserve Bank of
Richmond, 27 February 2000.
Hetzel, Robert L. “A Critical Appraisal of the Taylor Rule.”
Unpublished manuscript, Federal Reserve Bank of
Richmond, 11 February 2000.
Kozicki, Sharon. “How Useful Are Taylor Rules for Monetary
Policy?” Federal Reserve Bank of Kansas City Economic
Review, Second Quarter 1999, 84(2), pp. 5-33.
McCallum, Bennett T. “Recent Developments in the
Analysis of Monetary Policy Rules.” Federal Reserve
Bank of St. Louis Review, November/December 1999,
81(6), pp. 3-12.
Orphanides, Athanasios. “Monetary Policy Rules Based on
Real-Time Data.” Finance and Economics Discussion
Series No. 1998-3, Board of Governors of the Federal
Reserve System, January 1998.
Romer, Christina D. and Romer, David H. “Federal Reserve
Private Information and the Behavior of Interest Rates.”
American Economic Review, June 2000, 90(3), pp. 429-57.
Rotemberg, Julio J. and Woodford, Michael. “Interest Rate
Rules in an Estimated Sticky Price Model,” in John B.
Taylor, ed., Monetary Policy Rules. Chicago: The
University of Chicago Press, 1999, pp. 57-119.
Svensson, Lars E.O. “Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets.” European
Economic Review, June 1997, 41(6), pp. 1111-46.
___________ and Woodford, Michael. “Indicator Variables
for Optimal Policy.” Presented at Structural Change and
Monetary Policy Conference, Federal Reserve Bank of
San Francisco, 2000.
Taylor, John B. “Discretion Versus Policy Rules in Practice.”
Carnegie-Rochester Conference Series on Public Policy,
December 1993, 39, pp. 195-214.
Woodford, Michael. “Pitfalls of Forward-Looking Monetary
Policy.” American Economic Review, May 2000, 90(2), pp.
100-4.
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M AY / J U N E 2 0 0 1
FEDERAL RESERVE BANK OF ST. LOUIS
Toward a New
Paradigm in Open
Economy Modeling:
Where Do We Stand?
Lucio Sarno
n the last few decades, there have been a number of important developments, both theoretical and empirical, in open economy macroeconomics and exchange rate economics (see, for
example, Sarno and Taylor, 2001a, b). Also, the
increasing availability of high-quality macroeconomic and financial data has stimulated a large
amount of empirical work. While our understanding of exchange rates has improved as a result,
many challenges and questions remain. This paper
selectively surveys the recent literature on “new”
open economy macroeconomics. This literature,
stimulated by the work of Obstfeld and Rogoff
(hereafter OR) (1995), reflects the attempt by
researchers to formalize exchange rate determination in the context of dynamic general equilibrium
models with explicit microfoundations, nominal
rigidities, and imperfect competition.1
The main objective of this research program is
to develop a new workhorse model for open economy macroeconomic analysis. Relative to the still
ubiquitous Mundell-Fleming-Dornbusch (MFD)
model (Mundell, 1962, 1963; Fleming, 1962;
Dornbusch, 1976), new open economy models
offer a higher standard of analytical rigor coming
from fully specified microfoundations; they offer
the ability to perform welfare analysis and rigorously discuss policy evaluation in the context of a
framework that allows for market imperfections
and nominal rigidities. On the other hand, the
main virtue of the MFD model is its simpler analytical structure, which makes it easy to discuss in
I
Lucio Sarno is a reader in economics and finance at the Warwick
Business School, University of Warwick, and a research affiliate of
the Centre for Economic Policy Research, London. This paper was
written in part while the author was a visiting scholar at the Federal
Reserve Bank of St. Louis. The author thanks the United Kingdom
Economic and Social Research Council (ESRC) for providing financial support (grant No. L138251044) and Gaetano Antinolfi, James
Bullard, Giancarlo Corsetti, Brian Doyle, Fabio Ghironi, Peter Ireland,
Marcus Miller, Chris Neely, Michael Pakko, Neil Rankin, Mark Taylor,
and Dan Thornton for constructive comments. Paige Skiba provided
research assistance. The views expressed are those of the author
and should not be interpreted as reflecting those of any institution.
policy circles. Because the predictions of new
open economy models are sensitive to the particular specification of the microfoundations, policy
evaluation and welfare analysis depend on the
specification of preferences and nominal rigidities. In turn, this generates a need for the profession to agree on the “correct” or at least “preferable” specification of the microfoundations.
The present paper reviews the key contributions in new open economy macroeconomics in
the last five to six years, also assessing how the
intellectual debate stimulated by OR has led to
models that reflect reality more satisfactorily over
time. The paper also discusses some of the most
controversial issues that currently still prevent any
of the models in this area to emerge as a new paradigm for open economy modeling and describes
the directions taken by the latest literature.
The remainder of the paper is set out as follows. The first section provides a review of the
seminal paper in this literature, proposing the socalled redux model, while the second section covers a number of variants and generalizations of
the redux model that permit allowance for alternative nominal rigidities, pricing to market, alternative preference specifications, and alternative
financial markets structures. I then discuss some
stochastic extensions of these models, focusing on
their implications for the relationship between
uncertainty and exchange rates in the third section. Some new directions taken by the latest literature on stochastic open economy modeling are
described in the fourth section. A final section
presents some concluding remarks.
THE REDUX MODEL
The Baseline Model
OR (1995) is the study often considered as
having initiated the literature on new open economy macroeconomics (see, for example, Lane, 1999,
and Corsetti and Pesenti, 2001). However, a precursor of the OR (1995) model that deserves to be
noted here is the model proposed by Svensson
1
An early draft of this paper covered some of the models discussed
below in a more technical fashion. The preliminary technical version is available from the author upon request (Sarno, 2000). Walsh
(1998) also provides an excellent treatment of the redux model,
especially focusing on monetary issues. See also the comprehensive
textbook treatment of the early new open economy literature by
OR (1996) and its selective coverage by Lane (1999). For a treatment
of the role of imperfect competition in macroeconomic models, see
the survey by Dixon and Rankin (1994).
M AY / J U N E 2 0 0 1
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REVIEW
and van Wijnbergen (1989). They present a stochastic, two-country, neoclassical rational-expectations
model with sticky prices that are optimally set by
monopolistically competitive firms, where possible
excess capacity is allowed for to examine international spillover effects of monetary disturbances on
output. In contrast to the prediction of the MFD
model that a monetary expansion at home leads
to a recession abroad, the paper suggests that
spillover effects of monetary policy may be either
positive or negative, depending on the relative size
of the intertemporal and intratemporal elasticities
of substitution in consumption. It is also fair to say
that the need for rigorous microfoundations in
open economy models is not novel in new open
economy macroeconomics and has been emphasized by several papers prior to OR (1995); notable
examples are Lucas (1982), Stockman (1980, 1987),
and Backus, Kehoe, and Kydland (1992, 1994, 1995),
among others.
The baseline model proposed by OR (1995) is
a two-country, dynamic general equilibrium model
with microfoundations that allows for nominal
price rigidities, imperfect competition, and a continuum of agents who both produce and consume. Each agent produces a single differentiated
good. All agents have identical preferences, characterized by an intertemporal utility function that
depends positively on consumption and real
money balances but negatively on work effort;
effort is positively related to output. The exchange
rate is defined as the domestic price of the foreign
currency. The two countries are called Home and
Foreign, respectively.
Because the model assumes no impediments
to international trade, the law of one price (LOOP)
holds for each individual good and purchasing
power parity (PPP) holds for the internationally
identical aggregate consumption basket. PPP is the
proposition that national price levels should be
equal when expressed in a common currency; the
LOOP is the same proposition applied to individual goods rather than a consumption basket. Since
the real exchange rate is the nominal exchange
rate adjusted for relative national price levels, variations in the real exchange rate represent deviations from PPP. Hence, the LOOP and continuous
PPP imply a constant real exchange rate, while
long-run PPP (where temporary deviations from
PPP are allowed for) implies mean reversion in
the real exchange rate.
OR also assume that both countries can borrow and lend in an integrated world capital mar22
M AY / J U N E 2 0 0 1
ket. The only internationally traded asset is a riskless real bond, denominated in the consumption
good. Agents maximize lifetime utility subject to
their budget constraints (identical for domestic
and foreign agents). Utility maximization then
implies three clearly interpretable conditions. The
first is the standard Euler equation, which implies
a flat time path of consumption over time. The
second condition is the money market equilibrium
condition that equates the marginal rate of substitution of consumption for the services of real
money balances to the consumption opportunity
cost of holding real money balances (the nominal
interest rate); the representative agent directly benefits from holding money in the utility function
but loses the interest rate on the riskless bond as
well as the opportunity to eliminate the cost of
inflation. (Note that money demand depends on
consumption rather than income in this model.)
The third condition requires that the marginal utility of the higher revenue earned from producing
one extra unit of output equals the marginal disutility of the needed effort, and so can be interpreted as a labor-leisure trade-off equation.
In the special case when net foreign assets are
zero and government spending levels are equal
across countries, OR solve the model for income
and real money balances. Because this model is
based on a market structure with imperfect competition where each agent has some degree of
market power arising from product differentiation, the solutions of the model imply that steadystate output is suboptimally low. As the elasticity
of demand (say q ) increases, the various goods
become closer substitutes and, consequently, the
monopoly power decreases. As q approaches
infinity, output increases, tending to the level corresponding to a perfectly competitive market.
The main focus of OR (1995) is the impact of
a monetary shock on real money balances and
output. Under perfectly flexible prices, a permanent shock produces no dynamics and the world
economy remains in steady state (prices increase
by the same proportion as the money supply).
That is, an increase in the money supply has no
real effects and cannot remedy the suboptimal
output level. Money is neutral.2
2
Note that in the redux model and in a number of subsequent
papers, monetary shocks are discussed without a formalization of
the reaction functions of the monetary authorities. However, some
recent studies have formally investigated reaction functions in new
open economy macroeconomic models; see, for example, Ghironi
and Rebucci (2000) and the references therein.
FEDERAL RESERVE BANK OF ST. LOUIS
With prices displaying stickiness in the short
run, however, monetary policy may have real
effects. If the money supply increases, because
prices are fixed, the nominal interest rate decreases
and hence the exchange rate depreciates. This is
because, due to arbitrage in the foreign exchange
market, uncovered interest parity holds. Foreign
goods become more expensive relative to domestic goods, generating a temporary increase in the
demand for domestic goods and inducing an
increase in output. Consequently, monetary shocks
generate real effects on the economy. But how can
one ensure that producers are willing to increase
output? If prices are fixed, output is determined
by demand. Because a monopolist always prices
above the marginal cost, it is profitable to meet
unexpected demand at the fixed price. Noting that
in this model the exchange rate rises less than the
money supply, currency depreciation shifts world
demand toward domestic goods, which causes a
short-run rise in domestic income. Home residents consume some of the extra income, but,
because they want to smooth consumption over
time, they save part of it. Therefore, although in
the long run the current account is balanced, in
the short run Home runs a current account surplus. With higher long-run wealth, Home agents
shift from work to leisure reducing Home output.
Nevertheless, because Home agents’ real income
and consumption rise in the long run, the exchange rate does not necessarily depreciate.3
Unlike the scenario in a Dornbusch-type
model, the redux model does not yield exchange
rate overshooting. The exchange rate effect is
smaller the larger the elasticity of substitution, q ;
as q approaches infinity, Home and Foreign goods
become closer substitutes, producing larger shifts
in demand with the exchange rate changing only
slightly.
Finally, a monetary expansion leads to a firstorder welfare improvement.4 Because the price
exceeds the marginal cost in a monopolistic equilibrium, global output is inefficiently low. An
unanticipated money shock raises aggregate
demand stimulating production and mitigating
the distortion.
Summing up, in the redux model, monetary
shocks can generate persistent real effects, affecting consumption and output levels and the
exchange rate, although both the LOOP and PPP
hold. Welfare rises by equal amounts at home and
abroad after a positive monetary shock, and pro-
duction is moved closer to its efficient (perfectly
competitive market) level. Adjustment to the
steady state occurs within one period, but money
supply shocks can have real effects lasting beyond
the time frame of the nominal rigidities because
of the induced short-run wealth accumulation via
the current account. Money is not neutral, even in
the long run.
A Small Open Economy Version of the
Baseline Model
The baseline redux model and most of the
subsequent literature on new open economy
macroeconomics are based on a two-country
framework, which allows an explicit analysis of
international transmission channels and the
endogenous determination of interest rates and
asset prices. Nevertheless, similar, simpler models
may be constructed under the assumption of a
small open economy rather than a two-country
framework. In the small open economy version it
is also easier to allow a distinction between tradable and nontradable goods in the analysis. OR
(1995) provide such an example in their Appendix.
In this model, monopolistic competition characterizes the nontradable goods sector. The tradable
goods sector is characterized by a single homogenous tradable good that sells for the same price
all over the world, perfect competition, and flexible prices. The representative agent in the small
open economy, called Home, has an endowment
of the tradable good in constant quantity in each
period and monopoly power over the production
of one of the nontradable goods.
In this setup, a permanent monetary shock
does not generate a current account imbalance.
Because output of tradable goods is fixed, current
account behavior is determined by the time path
for tradables consumption, which, under logseparable preferences and a discount rate equal to
3
Again, note that in this model money demand depends on consumption rather than income. Thus, an increase in consumption
due to an increase in the nominal money supply raises money
demand by the same proportion.
4
In order to produce more, Home agents have to work harder. The
effects from reallocating consumption-production and leisure over
time are second-order, and the excess demand that leads to an
increase in production outweighs these effects. Of course, welfare
results depend upon the welfare function assumed. In the present
context, for example, it is important to note that inflation costs
(obviously generated by an expansionary monetary policy) are not
modeled explicitly.
M AY / J U N E 2 0 0 1
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REVIEW
the world interest rate, implies a perfectly flat
optimal time path for consumption. Hence, the
current account remains in balance. Unlike the
scenario in the baseline redux model, however,
exchange rate overshooting may occur in this
model. Since the monetary shock does not produce
a current account imbalance, money is neutral in
the long run and the nominal exchange rate rises
proportionately to the money stock. Because the
consumption elasticity of money demand is less
than unity (by assumption), the nominal exchange
rate overshoots its long-run level.5
Lane (1997) uses this small open economy
model to examine discretionary monetary policy
and the impact of openness (measured by the relative size of the tradables sector) on the equilibrium inflation rate. A more open economy (with a
large tradables sector) gains less from “surprise”
inflation because the output gain from a monetary
expansion is exclusively obtained in the nontradables sector and is relatively low. Since the equilibrium inflation rate under discretion is positively
related to the gains from “surprise” inflation (Barro
and Gordon, 1983), the model predicts that more
open economies have lower equilibrium inflation
rates (see also Kollmann, 1997; Velasco, 1997).
Lane (2001) further extends this model by
considering an alternative specification of the utility function under which monetary shocks generate current account imbalances. The sign of the
current account response is ambiguous, however;
in fact, it depends on the interplay between the
intertemporal elasticity of substitution, s, and the
intratemporal elasticity of substitution, q. s governs
the willingness to substitute consumption across
periods, while q governs the degree of substitutability between traded and nontraded consumption. If s<q, a positive monetary shock generates
a current account surplus; however, a current
account deficit occurs if s>q, whereas the current account remains in balance if s=q. Hence,
this model clearly illustrates how the results stemming from this class of models are sensitive to the
specification of the microfoundations.
The implications of small open economy
models of this class seem plausible. While the relevant literature (and consequently the rest of this
survey) largely uses a two-country global economy framework, I think it might also be worthwhile to pursue research based on the small open
economy assumption. Indeed, the small open
economy assumption is plausible for most coun24
M AY / J U N E 2 0 0 1
tries, except the United States. Furthermore, testing
the empirical implications of the small open economy models discussed in this section represents a
new line of research for applied economists.
RETHINKING THE REDUX MODEL
Nominal Rigidities
As mentioned earlier, subsequent work has
modified many of the assumptions of the redux
model. In this section, I discuss modifications based
on the specification of nominal rigidities. The open
economy literature surveyed here provides some
novel thoughts in this context and might generate
evidence for choosing among alternative specifications of stickiness in macroeconomic models.
Whether the extension from closed to open economy models does help to achieve consensus on
the specification of nominal rigidities remains to be
seen (see, for example, the arguments presented by
OR, 2000a, discussed below).
With respect to nominal rigidities, the redux
model assumes that prices are set one period in
advance, which implies that the adjustment to
equilibrium is completed after one period. As
Corsetti and Pesenti (2001) emphasize, however, if
price stickiness is motivated by fixed menu costs,
firms have an incentive to adjust prices immediately after a shock if the shock is large enough to violate their participation cost by raising the marginal
cost above the price. Hence, the redux analysis may
be seen as plausible only within the relevant range
of shocks.
Hau (2000) generalizes the redux model in
three ways to investigate the role of factor price
(wage) rigidities and nontradables for the international transmission mechanism. First, following
Blanchard and Kiyotaki (1987), the model allows
for factor markets and for nominal rigidities originating from sticky factor prices (wages). Second,
Hau assumes flexible price setting in local currency and does not assume international goods arbitrage. While the LOOP still holds because of optimal monopolistic price setting, nontradables in
the consumer price index produce deviations
from PPP. Third, unlike the scenario in the redux
analysis, Hau also allows for nontradable goods.
The main result of the paper is that factor price
rigidities have similar implications to rigid domes5
Indeed, this is exactly the same overshooting condition derived in
the Dornbusch (1976) model.
FEDERAL RESERVE BANK OF ST. LOUIS
tic producer prices. In some sense, the results of
the redux analysis are confirmed in the context of
a market structure with factor price rigidities. However, nontradables modify the transmission mechanism in important ways. A larger nontradables
share implies that exchange rate movements are
magnified, since the money market equilibrium
relies on a short-run price adjustment carried out
by fewer tradables. This effect is interesting since it
may help explain the observed high volatility of the
nominal exchange rate relative to price volatility.
Within the framework of price level rigidities,
however, a more sophisticated way of capturing
price stickiness is through staggered price setting
that allows smooth, rather than discrete, aggregate
price level adjustment. Staggering price models of
the type developed by, among others, Taylor (1980)
and Calvo (1983) are classic examples. Kollmann
(1997) calibrates a dynamic open economy model
with both sticky prices and sticky wages and then
explores the behavior of exchange rates and prices
in response to monetary shocks with predetermined
price and wage setting and Calvo-type nominal
rigidities. His results suggest that Calvo-type nominal rigidities match very well the observed high
correlation between nominal and real exchange
rates and the smooth adjustment in the price level,
but they match less well correlations between output and several other macroeconomic variables.
Chari, Kehoe, and McGrattan (CKM) (1998,
2000) link sticky price models to the behavior of
the real exchange rate in the context of a new open
economy macroeconomic model. They start by
noting that the data show large and persistent deviations of real exchange rates from PPP that appear
to be driven primarily by deviations from the LOOP
for tradable goods. That is, real and nominal
exchange rates are about six times more volatile
than relative price levels and both are highly persistent, with first-order serial correlations of about
0.85 and 0.83, respectively, at annual frequency.
CKM then develop a sticky price model with pricediscriminating monopolists that produces deviations from the LOOP for tradable goods. However,
their benchmark model, which has prices set for
one quarter at a time and a unit consumption elasticity of money demand, does not come close to
reproducing the serial correlation properties of real
and nominal exchange rates noted above. A model
in which producers set prices for six quarters at a
time and with a consumption elasticity of money
demand of 0.27 does much better in generating
persistent and volatile real and nominal exchange
rates. The serial correlations of real and nominal
exchange rates are 0.65 and 0.66, respectively, and
exchange rates are about three times more volatile
than relative price levels.
In a closely related paper, Jeanne (1998)
attempts to assess whether money can generate
persistent economic fluctuations in a dynamic
general equilibrium model of the business cycle.
Jeanne shows that a small nominal friction in the
goods market can make the response of output to
monetary shocks large and persistent if it is amplified by real-wage rigidity in the labor market. He
also argues that, for plausible levels of real-wage
rigidity, a small degree of nominal stickiness may be
sufficient for money to produce economic fluctuations as persistent as those observed in the data.6
OR (2000a), discussed in detail later in this
paper, develop a stochastic new open economy
macroeconomic model based on sticky nominal
wages, monopolistic competition, and exporterscurrency pricing. Solving explicitly the wage-setting
problem under uncertainty allows the analysis of
the welfare implications of alternative monetary
regimes and their impact on expected output and
terms of trade. To motivate their model, OR show
that observed correlations between terms of trade
and exchange rates appear to be more consistent
with their assumptions about nominal rigidities
than with the alternative specification based on
local-currency pricing.
I now turn to a discussion of the reformulations
of the redux model based on the introduction of
pricing to market.
Pricing to Market
While the redux model assumes that the LOOP
holds for all tradable goods, a number of researchers have questioned the model on the
ground that deviations from the LOOP across international borders appear to be larger than can be
explained by geographical distance or transport
costs (see, for example, Engel, 1993, and Engel and
Rogers, 1996). Some authors have therefore extended the redux model by combining international segmentation with imperfectly competitive
firms and local-currency pricing (essentially pricing to market or PTM). Krugman (1987) used the
term PTM to characterize price discrimination for
6
See also Andersen (1998), Benigno (1999), and Bergin and Feenstra
(1999, 2000).
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certain types of goods (such as automobiles and
many types of electronics) where international arbitrage is difficult or perhaps impossible. This may
be due, for example, to differing national standards
(for example, 100-volt light bulbs are not used in
Europe and left-hand-side-drive cars are not popular in the United Kingdom, Australia, or Japan).
Further, monopolistic firms may be able to limit or
prevent international goods arbitrage by refusing
to provide warranty service in one country for
goods purchased in another. To the extent that
prices cannot be arbitraged, producers can discriminate across different international markets.
Studies allowing for PTM typically find that
PTM may play a central role in exchange rate determination and in international macroeconomic
fluctuations. This happens because PTM acts to
limit the pass-through from exchange rate movements to prices, reducing the “expenditure switching” role of exchange rate changes and potentially
generating greater exchange rate variability than
would be obtained in models without PTM. Also,
nominal price stickiness, in conjunction with PTM,
magnifies the response of the exchange rate to
macroeconomic fundamentals shocks. Further, by
generating deviations from PPP, PTM models also
tend to reduce the comovement in consumption
across countries while increasing the comovement
of output, fitting some well-known empirical regularities (see Backus, Kehoe, and Kydland, 1992).
Finally, the introduction of PTM has important welfare implications for the international transmission
of monetary policy shocks, as discussed below.
Betts and Devereux (2000b), for example, characterize PTM by assuming that prices of many
goods are set in the local currency of the buyer
and do not adjust at high frequency. Consequently,
real exchange rates move with nominal exchange
rates at high frequency. These assumptions also
imply that price/cost markups fluctuate endogenously in response to exchange rate movements
rather than nominal prices (see also Knetter, 1993,
on this point). In the Betts-Devereux framework,
traded goods are characterized by a significant degree of national market segmentation and trade is
carried out only by firms. Households cannot arbitrage away price differences across countries, and
firms engage in short-term nominal price setting.
Therefore, prices are sticky in terms of the local
currency.7
The Betts-Devereux model is based on an
economy with differentiated products and assumes
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that firms can price-discriminate across countries.
With a high degree of PTM (that is, when a large
fraction of firms engages in PTM), a depreciation of
the exchange rate has little effect on the relative
price of imported goods faced by domestic consumers. This weakens the allocative effects of exchange rate changes relative to a situation where
prices are set in the seller’s currency; in the latter
case, pass-through of exchange rates to prices is
immediate. Hence, PTM reduces the expenditure
switching effects of exchange rate depreciation,
which generally implies a shift of world demand
toward the exports of the country whose currency
is depreciating. Because domestic prices show little response to exchange rate depreciation under
PTM, the response of the equilibrium exchange
rate may be substantially magnified and, consistent with well-known observed empirical regularities, exchange rates may vary more than relative
prices.
PTM also has implications for the international
transmission of macroeconomic shocks. In the absence of PTM, for example, monetary disturbances
tend to generate large positive comovements of
consumption across countries but large negative
comovements of output. However, PTM reverses
the ordering: the deviations from PPP induced by
PTM make consumption comovements fall. At the
same time, the elimination of expenditure switching effects of the exchange rate enhances comovements of output across countries.
In terms of welfare, recall that the framework
based on the LOOP and PPP generally suggests
that an unanticipated monetary expansion raises
welfare of all agents at home and abroad. With
PTM, however, a domestic monetary expansion
raises home welfare but reduces foreign welfare
and monetary policy is a “beggar-thy-neighbor”
instrument. Therefore, the PTM framework, unlike the framework based on the LOOP and PPP,
provides a case for international monetary policy
coordination.
Overall, the PTM framework suggests that
goods market segmentation might help explain international quantity and price fluctuations and
may have important implications for the international transmission of economic shocks, policy,
and welfare.
7
The model of Betts and Devereux (2000b) is used as a representative of this class of PTM models in this section. Other examples of
models adopting PTM are Betts and Devereux (1996, 1997, 1999,
2000a); CKM (1998, 2000); and Bergin and Feenstra (1999, 2000).
FEDERAL RESERVE BANK OF ST. LOUIS
The Indeterminacy of the Steady State
In the framework proposed by OR (1995), the
current account plays a crucial role in the transmission of shocks. However, the steady state is indeterminate and both the consumption differential
between countries and an economy’s net foreign
assets are nonstationary. After a monetary shock,
the economy will move to a different steady state
until a new shock occurs. When the model is loglinearized to obtain closed-form solutions of the
endogenous variables, one is approximating the
dynamics of the model around a moving steady
state. This makes the conclusions implied by the
model questionable. In particular, the reliability of
the log-linear approximations is low because variables wander away from the initial steady state.
Many subsequent variants of the redux model
de-emphasize the role of net foreign assets accumulation as a channel of macroeconomic interdependence between countries. This is done by
assuming that (i) the elasticity of substitution
between domestic and foreign goods is unity or
(ii) financial markets are complete. Both of these
assumptions imply that the current account does
not react to shocks (see, for example, Corsetti and
Pesenti, 2001, and OR, 2000a).8 While this framework achieves the desired result of determinacy
of the steady state, it requires strong assumptions—
(i) or (ii) above—to shut off the current account,
which is unrealistic. In a sense these solutions circumvent the problem of indeterminacy, but they
do not solve it.
Ghironi (2000a) provides an extensive discussion of the indeterminacy and nonstationarity
problems in the redux model. Ghironi also provides a tractable two-country model of macroeconomic interdependence that does not rely on
either of the above assumptions in that the elasticity
of substitution between domestic and foreign
goods can be different from unity and that financial markets are incomplete, consistent with reality. Using an overlapping generations structure,
Ghironi shows how there exists a steady state, endogenously determined, to which the world economy reverts following temporary shocks. Accumulation of net foreign assets plays a role in the
transmission of shocks to productivity. Finally,
Ghironi also shows that shutting off the current
account may lead to large errors in welfare comparisons, which calls for rethinking of several results in this literature.
The issue of indeterminacy of the steady state
deserves further attention from researchers in this
area.
Preferences
While the explicit treatment of microfoundations is a key advantage of new open economy
macroeconomic models relative to the MFD model,
the implications of such models depend on the
specification of preferences. One convenient assumption in the redux model is the symmetry with
which home and foreign goods enter preferences
in the constant-elasticity-of-substitution (CES) utility function. Corsetti and Pesenti (2001) extend the
redux model to investigate the effects of a limited
degree of substitution between home and foreign
goods. In their baseline model, the LOOP still holds
and technology is described by a Cobb-Douglas
production function, with a unit elasticity of substitution between home and foreign goods and
constant income shares for home and foreign
agents. The model illustrates that the welfare effects of expansionary monetary and fiscal policies
are related to internal and external sources of economic distortion, namely, monopolistic supply in
production and monopoly power of a country. For
example, an unanticipated exchange rate depreciation can be “beggar-thyself” rather than “beggarthy-neighbor” since gains in domestic output are
offset by losses in consumers’ purchasing power
and a deterioration in terms of trade. Also, openness is not inconsequential: smaller and more open
economies are more prone to inflationary consequences. Fiscal shocks, however, are generally
“beggar-thy-neighbor” in the long run, but they
raise domestic demand in the short run for given
terms of trade. These results provide a role for international policy coordination, which is not the
case in the redux model.9,10
An important assumption in the redux model
is that consumption and leisure are separable. This
8
This is a problem often encountered in the international real business cycles literature. Note, however, that the role of current
account dynamics in generating persistent effects of transitory
shocks has often been found to be quantitatively unimportant in
this literature. See the discussion on this point by Baxter and
Crucini (1995) and Kollmann (1996).
9
Recall that the redux model has the unrealistic implication that the
optimal monetary surprise is infinite, which is of course not the
case in the Corsetti-Pesenti model.
10
Devereux (1999), Doyle (2000), Tille (1998a, b), Betts and Devereux
(2000a), and Benigno (2001), among others, represent attempts to
model explicitly international policy coordination in variants of the
Corsetti-Pesenti model. See also OR (2000b).
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assumption is not compatible, however, with a balanced growth path if trend technical progress is
confined to the market sector. As a country becomes richer, labor supply gradually declines, converging to a situation in which labor supply is zero,
unless the intertemporal elasticity of substitution
is unity. CKM (1998), for example, employ a preference specification with nonseparable consumption and leisure (which is fairly standard, for example, in the real business cycles literature). This
preference specification is compatible with a balanced growth path and is also consistent with the
high real exchange rate volatility that is observed
in the data. A more elastic labor supply and a
greater intertemporal elasticity of substitution in
consumption generates more volatile real exchange
rates. Hence, this preference specification provides
more plausible implications for the short-run
dynamics of several macroeconomic variables
relative to the redux model and better matches
some observed regularities.11
While the discussion in this subsection has focused on only two issues with regard to the specification of preferences (the degree of substitutability
of home and foreign goods in consumption and
the separability of consumption and leisure in utility), the results of models with explicit microfoundations may depend crucially on the specification
of the utility function in other ways. Relaxing the
symmetry assumption in the utility function and
allowing for nonseparable consumption and
leisure, for example, would yield more plausible
and more general utility functions. Of course, there
are other related important issues and, in this respect, the closed economy literature can lend ideas
on how to proceed; see, for example, the large and
growing closed economy literature on habit formation and home production.
Financial Markets Structure
The redux model assumes that there is international trade only in a riskless real bond, and
hence financial markets are not complete. Deviations from this financial markets structure have
been examined in several papers. CKM (1998)
compare, in the context of their PTM model, the
effects of monetary shocks under complete markets and under a setting where trade occurs only
in one noncontingent nominal bond denominated
in the domestic currency. Their results show that
the redux model is rather robust in this case. In
fact, incompleteness of financial markets appears
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to imply small differences for the persistence of
monetary shocks.12
A related study by Sutherland (1996) analyzes
trading frictions (which essentially allow for a differential between domestic and foreign interest
rates) in the context of an intertemporal general
equilibrium model where financial markets are
incomplete and the purchase of bonds involves
convex adjustment costs. Goods markets are perfectly competitive and goods prices are subject to
Calvo-type sluggish adjustment. Sutherland shows
that barriers to financial integration have a larger
impact on output the greater the degree of price
inertia. With substantial price inertia, output
adjusts slowly and more agents smooth their consumption pattern via international financial markets. Sutherland’s simulations suggest that financial market integration increases the volatility of a
number of variables when shocks originate from
the money market but decreases the volatility of
most variables when shocks originate from real
demand or supply; these results also hold in the
generalization of Sutherland’s model by Senay
(1998). For example, a positive domestic monetary
shock induces a domestic interest rate decline
and, therefore, a negative interest rate differential
with the foreign country. In turn, the negative
interest rate differential produces a smaller
exchange rate depreciation and a larger jump in
relative domestic consumption. This implies that
domestic output rises less in this model than in
the baseline redux model.
OR (1995) defend their assumptions regarding
the financial markets structure of the redux model
stating that it would seem incoherent to analyze
imperfections or rigidities in goods markets, while
at the same time assuming that international capital markets are complete. Indeed, one may argue
that, if there were complete international risk
sharing, it is unclear how price or wage rigidities
could exist. Nevertheless, the assumption of full
international capital integration is very controversial. While many economists would agree that the
degree of financial integration has increased over
11
A further modification of the redux model considered by
researchers involves the introduction of nontradables in the analysis, which typically implies an increase in the size of the initial
exchange rate response to a monetary shock; see, for example,
Ghironi (2000b), Hau (2000), and Warnock (1999).
12
Note, however, that none of the models discussed in this paper have
complete markets in the Arrow-Debreu sense, with the possible
exception of CKM (1998).
FEDERAL RESERVE BANK OF ST. LOUIS
time (at least across major industrialized countries),
it is perhaps fair to say that there are frictions in
financial markets (see Obstfeld, 1995). Given the
controversies over what may constitute a realistic
financial markets structure, the analysis of the
impact of barriers to financial integration remains
an avenue of research in its own right.
The Role of Capital
The literature has largely neglected the role of
capital in new open economy models. For example,
competitive models with capital can deliver effects
of supply shocks similar to those typically found
in monopolistically competitive models with
endogenous utilization of capital (see, for example, Finn, 2000).13 CKM (1998, 2000) also argue
that capital (omitted in the redux model and most
subsequent variants of it) may play an important
role because monetary shocks can cause investment booms by reducing the short-term interest
rate and hence generate a current account deficit
(rather than a surplus, as in the redux model).
Explicitly allowing for capital in new open economy models is an important immediate avenue for
future research.
STOCHASTIC NEW OPEN ECONOMY
MACROECONOMICS
Recently, the certainty equivalence assumption that characterizes much of the literature discussed above (including the redux model) has
been relaxed. While certainty equivalence allows
researchers to approximate exact equilibrium
relationships, it “precludes a serious welfare analysis of changes that affect the variance of output”
(Kimball, 1995, p. 1243). Following this line of
reasoning, OR (1998) first extend the redux model
and the work by Corsetti and Pesenti (2001) to a
stochastic environment. More precisely, the innovation in OR (1998) involves moving away from
the analysis of only unanticipated shocks.14
Risk and Exchange Rates
The OR (1998) model may be interpreted as a
sticky-price monetary model in which risk has an
impact on asset prices, short-term interest rates,
the price-setting decisions of individual producers, expected output, and international trade
flows. This approach allows OR to quantify the
welfare tradeoff between alternative exchange
rate regimes and to relate such tradeoff to a coun-
try’s size. Another important finding of this model
is that exchange rate risk affects the level of the
exchange rate. Not surprisingly, as discussed
below, the model has important implications for
the behavior of the forward premium and for the
forward discount bias.
The setup of the OR (1998) model adds uncertainty to the redux model. Most results are standard and qualitatively identical to those of the
redux model. However, one of the most original
results of this approach is the equation describing
the equilibrium exchange rate. To obtain the equilibrium exchange rate, OR (1998) assume that
Home and Foreign have equal trend inflation rates
(equal to the long-run nominal interest rates
through the Fisher equation) and use conventional
log-linearizations (in addition to the assumption
that PPP holds) to obtain an equation of nominal
exchange rate determination. This equation may
be interpreted as a monetary-model-type equation where conventional macroeconomic fundamentals determine the exchange rate. Also, this
exchange rate equation is the same as in the
redux model, except for a time-varying risk premium term. Under the assumption of no bubbles,
the solution of the model suggests that a level risk
premium enters the exchange rate equation. In
some sense, this model may explain the failure of
conventional monetary models of exchange rate
determination in terms of an omitted variable in
the exchange rate equation, namely, exchange
rate risk; a similar result was obtained by Hodrick
(1989) in the context of a cash-in-advance flexibleprice exchange rate model. For example, less
relative risk of investments in the Home currency
induces a fall in the domestic nominal interest
rate and an appreciation of the domestic currency,
capturing the idea of a “safe haven” effect on the
Home currency.
For reasonable interest rates, a rise in Home
monetary variability induces both a fall in the
13
Finn (2000) demonstrates that a theory of perfect competition,
which views capital utilization as the avenue through which energy
enters into the model economy, can explain the observed effects of
energy price increases on economic activity, which Rotemberg and
Woodford (1996) and several subsequent studies defined as inexplicable without a theory of imperfect competition.
14
Note, however, that I am using the term stochastic loosely here.
Even in approximated dynamics with certainty equivalence, models
are stochastic. Evaluations of the first-order effects of second
moments (noncertainty equivalence) recognize an aspect of
stochastic models that is often neglected, but this does not by itself
define a stochastic model.
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level of the exchange rate risk premium and a fall
in the forward premium (the latter fall is shown to
be much larger in magnitude). This result contradicts the conventional wisdom that financial markets attach a positive risk premium to the currency
with higher monetary volatility. The intuition is
explained by OR (1998) as follows:
[A] rise in Home monetary volatility may
lead to a fall in the forward premium, even
holding expected exchange rate changes
constant. Why? If positive domestic monetary shocks lead to increases in global consumption, then domestic money can be a
hedge, in real terms, against shocks to consumption. (The real value of Home money
will tend to be unexpectedly high in states
of nature where the marginal utility of
consumption is high.) Furthermore—and
this effect also operates in a flexible-price
model—higher monetary variability raises
the expectation of the future real value of
money, other things equal. (p. 24)
This result provides a novel theoretical explanation of the forward premium puzzle. Not only
should high interest rates not necessarily be associated with expected depreciation, but the opposite may also be true, especially for countries with
similar trend inflation rates.
Nevertheless, the results produced by this
model may well depend critically on the specification of the microfoundations and are, therefore,
subject to the same caveats raised by the literature
questioning the appropriateness of the redux
specification. Thus, it is legitimate to wonder how
adopting the other specifications (alternative
specifications of utility, different nominal rigidities, etc.) described earlier would affect the results
of the OR (1998) stochastic model. The next subsection discusses, for example, the changes
induced by the introduction of PTM in this model.
Related Studies
The OR (1998) analysis described above is
based on the following assumptions: (i) that producers set prices in their own currency, (ii) that the
price paid by foreigners for home goods (and the
price paid by domestic residents for foreign goods)
varies instantaneously when the exchange rate
changes, and (iii) that the LOOP holds. Devereux
and Engel (1998) extend the OR (1998) analysis by
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assuming PTM and that producers set a price in
the home currency for domestic residents and in
the foreign currency for foreign residents. Hence,
when the exchange rate fluctuates, the LOOP does
not hold. The risk premium depends on the type
of price-setting behavior of producers. Devereux
and Engel compare the agent’s welfare between
fixed and flexible exchange rate arrangements and
find that exchange rate systems matter not only
for the variances of consumption, real balances,
and leisure but also for their mean values once risk
premia are incorporated into pricing decisions.
Since PTM insulates consumption from exchange
rate fluctuations, floating exchange rates are less
costly under PTM than under producer currency
pricing. Consequently, a flexible regime generally
dominates a pegged regime.15
Engel (1999) makes four points in summarizing the evidence on the foreign exchange risk premium in this class of general equilibrium models.
First, while the existence of a risk premium in
flexible-price general equilibrium models depends
on the correlation of exogenous monetary shocks
and aggregate supply shocks, the risk premium
arises endogenously in sticky-price models. Second,
the distribution of aggregate supply shocks does
not affect the foreign exchange risk premium in
sticky-price models. Third, given that the risk premium depends on the prices faced by consumers,
when the LOOP does not hold there is no unique
foreign exchange risk premium since producers
set prices in consumers’ currencies. Fourth, standard stochastic dynamic general equilibrium
models do not usually imply large risk premia.
The common denominator in these models is
that the exchange rate risk premium is an important determinant of the equilibrium level of the
exchange rate. It remains an open question
whether one could build a sticky-price model
capable of convincingly explaining the forward
premium puzzle. Nevertheless, this seems a
promising avenue for future research.
NEW DIRECTIONS: THE SOURCE OF
NOMINAL RIGIDITIES AND THE
CHOICE BETWEEN LOCAL AND
FOREIGN CURRENCY PRICING
OR (2000a) may have again set new directions
for stochastic open economy models of the class
15
See also Bacchetta and van Wincoop (1998).
FEDERAL RESERVE BANK OF ST. LOUIS
discussed in this paper. They start by noting that
the possibilities for modeling nominal rigidities
are more numerous in a multicurrency international economy than in a single-money closed
economy setting and that, in an international setting, it is natural to consider the possibility of segmentation between national markets. OR address
the empirical issue of whether local currency pricing or foreign currency pricing is closer to reality.
OR argue that, if imports are invoiced in the importing country’s currency, unexpected currency
depreciations should be associated with improvements (rather than deteriorations) in the terms of
trade. They then show that this implication is inconsistent with the data. Indeed, their evidence suggests that aggregate data may favor a traditional
framework in which exporters largely invoice in
home currency and nominal exchange rate changes
have significant short-run effects on international
competitiveness and trade.
The main reservations of OR about the PTM–
local currency pricing framework employed by
several papers in this literature are captured by the
following observations. First, a large fraction of
measured deviations from the LOOP results from
nontradable components incorporated in consumer price indices for supposedly traded goods
(for example, rents, distribution services, advertising, etc.); it is not clear whether the extreme market segmentation and pass-through assumptions of
the PTM–local currency pricing approach are necessary to explain the close association between
deviations from the LOOP and exchange rates. Second, price stickiness induced by wage stickiness is
likely to be more important in determining persistent macroeconomic fluctuations since trade invoicing cannot generate sufficiently high persistence.
(Invoicing largely applies to contracts of 90 days
or less.) Third, the direct evidence on invoicing is
largely inconsistent with the view that exporters
set prices mainly in importers’ currencies (see, for
example, ECU Institute, 1995); the United States is,
however, an exception. Fourth, international evidence on markups is consistent with the view
that invoicing in exporters’ currencies is the predominant practice (see, for example, Goldberg and
Knetter, 1997).
OR (2000a) build their stochastic dynamic
open economy model with nominal rigidities in
the labor market (rationalized on the basis of the
first two observations above) and foreign currency
pricing (rationalized on the basis of the last two
observations above). They consider a standard twocountry global economy where Home and Foreign
produce an array of differentiated tradable goods
(Home and Foreign have equal size). In addition,
each country produces an array of differentiated
nontraded goods. Workers set next period’s
domestic-currency nominal wages and then meet
labor demand in the light of realized economic
shocks. Prices of all goods are completely flexible.
OR provide equilibrium equations for preset
wages and a closed-form solution for each endogenous variable in the model as well as solutions
for variances and for utility. In particular, the solution for the exchange rate indicates that a relative
Home money supply increase that occurs after
nominal wages are set would cause an overshooting depreciation in the exchange rate. A fully
anticipated change, however, causes a precisely
equal movement in the wage differential and in
the exchange rate.
In this setup, OR show welfare results on two
fronts. First, they show that constrained-efficient
monetary policy rules replicate the flexible-price
equilibrium and feature a procyclical response to
productivity shocks.16 For example, a positive productivity shock that would elicit greater labor supply and output under flexible wages optimally
induces an expansionary Home monetary response
when wages are set in advance. The same shock
elicits a contractionary Foreign monetary response,
but the net global monetary response is always
positive. Also, optimal monetary policy allows the
exchange rate to fluctuate in response to crosscountry differences in productivity shocks. This
conclusion is similar to the result obtained by King
and Wolman (1996) in a rational expectations
model where monetary policy has real effects
because imperfectly competitive firms are constrained to adjust prices only infrequently and to
satisfy all demand at posted prices. In the KingWolman sticky-price model, it is optimal to set
monetary policy so that the nominal interest rate
is close to zero (that is, neutralizing the effect of
the sticky prices), replicating in an imperfectly
competitive model the result that Friedman found
under perfect competition. Under a perfect infla16
These monetary policy rules are (i) constrained since they are
derived by maximizing an average of Home and Foreign expected
utilities subject to the optimal wage-setting behavior of workers and
price-setting behavior of firms described in the model, and (ii) efficient since the market allocation cannot be altered without making
one country worse off, given the constraints.
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tion target, the monetary authority makes the
money supply evolve so that a model with sticky
prices behaves much like one with flexible prices.
Second, OR calculate the expected utility for
each of three alternative monetary regimes,
namely, an optimal floating rate regime, world
monetarism (under which two countries fix the
exchange rate while also fixing an exchange rate–
weighted average of the two national money supplies), and an optimal fixed rate regime. The outcome is that the expected utility under an optimal
floating-rate regime is highest. This result is intuitively obvious given that optimal monetary policy
in this model involves allowing the exchange rate
to fluctuate in response to cross-country differences in productivity shocks. Fixed-rate regimes
would only be worthwhile if productivity shocks
at home and abroad were perfectly correlated.17
The OR (2000a) model addresses several theoretical and policy questions, including welfare
analysis under alternative nominal regimes. The
assumption that nominal exchange rate movements shift world demand between countries in
the short run, which plays a crucial role in the
traditional MFD model, is shown to be consistent
with the facts and can reasonably be used as a
building block in stochastic open economy models. Needless to say, this approach warrants further generalizations and refinements. In particular, note that the current account is shut off in OR
(2000a) to avoid the indeterminacy problem discussed earlier. However, shutting off the current
account makes the model less plausible from an
empirical point of view since it distorts the
dynamics of the economy being modeled.
It is worth noting that the new open economy
macroeconomics literature to date has (implicitly
or explicitly) assumed that there are no costs of
international trade. Nevertheless, the introduction
of some sort of international trade costs (including, among others, transport costs, tariffs, and
nontariff barriers) may be key in understanding
how to improve empirical exchange rate models
and in explaining several unresolved puzzles in
international macroeconomics and finance. While
the allowance of trade costs in open economy
modeling is not a new idea and goes back at least
to Samuelson (1954), OR (2000c) have recently
stressed the role of trade costs in open economy
macroeconomics. Indeed, OR (2000c) present
something of a “unified theory” that helps elucidate what the profession may be missing when
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trying to explain several puzzling empirical findings using trade costs as the fundamental modeling feature, with sticky prices playing a distinctly
secondary role. It is hoped that future research in
new open economy macroeconomics follows the
suggestion of OR (2000c) to make explicit allowance for non-zero international trade costs.
CONCLUSIONS
In this paper, I have selectively reviewed the
recent literature on new open economy macroeconomics, which has been growing exponentially
in the last five years or so. The increasing sophistication of stochastic open economy models allows
rigorous welfare analysis and provides new explanations of several puzzles in international macroeconomics and finance. Whether this approach
will become the new workhorse model for open
economy macroeconomics, whether a preferred
specification within this class of models will be
reached, and whether this approach will provide
insights on developing better-fitting empirical
exchange rate models are open questions.
Although the theory in the spirit of new open
economy macroeconomics is developing very
rapidly, there is little effort at present to test the
predictions of new open economy models.
Theorists working in this area should specify
exactly which empirical exchange-rate equations
they would have empiricists estimate. If there is to
be consensus in the profession on a particular
model specification, this theoretical apparatus has
to produce clear estimable equations.18
Agreeing on a particular new open economy
model is hardly possible at this stage. This is the
case not least because it requires agreeing on
assumptions which are often difficult to test
directly (such as the specification of the utility
function) or because they concern issues on
which economists have strong beliefs on which
they have not often been willing to compromise
(such as whether nominal rigidities originate from
the goods market or the labor market or whether
17
Indeed, the results suggest that the difference between the expected
utility under an optimal floating-rate regime and the expected utility under an optimal fixed-rate regime may not be too large if the
variance of productivity shocks is very small or the elasticity of utility with respect to effort is very large.
18
A first step toward new open economy macroeconometrics has
been made, for example, by Ghironi (2000c). I am also currently
investigating empirical exchange rate equations inspired by the
new open economy macroeconomics literature.
FEDERAL RESERVE BANK OF ST. LOUIS
nominal rigidities exist at all). Achieving a new
paradigm for open economy modeling is, however, a major challenge which lies ahead for the profession. While the profession shows some convergence toward a consensus approach in macroeconomic modeling (where the need for microfoundations, for example, seems widely accepted), it
seems very unlikely that a consensus model will
emerge in the foreseeable future.
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36
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FEDERAL RESERVE BANK OF ST. LOUIS
A Simple Model of
Limited Stock Market
Participation
Hui Guo
he 1998 Survey of Consumer Finance data
shows that only 48.8 percent of U.S. households owned stocks, either (i) directly or (ii)
indirectly through mutual funds. In addition, there
is a close relationship between shareholding and
wealth. In 1998, 93 percent of the richest 1 percent of the population owned stocks; the richest
10 percent owned 85 percent of total stocks and
mutual funds, compared with 51 percent of total
savings deposits. Meanwhile, the average stock
return is “abnormally” higher than the average
government bond return.1 In this paper, I try to
explain this shareholding puzzle—why many people do not hold stocks given that stocks outperform government bonds by a large margin.
It is costly to collect and process information
about stock markets. Bertaut (1997) finds that
better-educated people are more likely to hold
stocks, even after controlling for variables such as
wealth, current income, and unemployment risk.
He interprets education as a measure of the ability
to process information about the market and
investment opportunities. However, information
costs are not the only reason for limited stock
market participation. Rather, recent research
emphasizes that people tend to hold fewer risky
assets such as stocks in their portfolio if they are
more vulnerable to income shocks. For example,
borrowing constraints (Guiso, Jappelli, and
Terlizzese, 1996), labor income risks (VissingJorgensen, 1998b), home ownership (Fratantoni,
1998), and entrepreneurial risks (Heaton and
Lucas, 2000) are found to deter stock market entry.
Moreover, these factors have smaller effects on
people who have larger wealth. Holtz-Eakin,
Joulfaian, and Rosen (1994) find that people are
willing to take more risks if they receive a large
inheritance.
In this paper, I develop a life-cycle model to
show how market imperfections may interact with
T
heterogeneous wealth to generate limited stock
market participation. Many factors, such as
successful entrepreneurial effort, life-cycle
savings, precautionary savings, and inheritance,
explain wealth inequality. To keep the model manageable, I focus on three key elements, namely,
different investment opportunities (stocks and
bonds), credit market imperfections, and inheritance. In the model, although the stock return is
higher than the bond return, only people with
wealth over a certain threshold own stocks. This
occurs for two reasons. First, there is a fixed cost
to entering the stock market. Second, people face
a borrowing rate that is higher than the saving rate
so that they cannot arbitrage by selling bonds and
buying stocks. As a result, wealthy households
accumulate more wealth and pass on a greater
inheritance to their families than poor households
do. In the long run, wealth is unequally distributed
and wealthy households own almost all stocks.
Some other mechanisms have explained limited stock market participation. Becker (1980)
shows that the most patient agent owns all capital
in the long run. Allen and Gale (1994) argue that
the less risk-averse person is more likely to hold
stocks. Constantinides, Donaldson, and Mehra
(2000) stress the life-cycle pattern of shareholdings.
Asset returns and limited stock market participation are two closely related issues. However,
recent research (i.e., Constantinides, Donaldson,
and Mehra, 2000; Polkovnichenko, 2000; and
Yaron and Zhang, 2000) has had difficulty explaining the two simultaneously in general equilibrium
models. Therefore, I address asset returns and limited stock market participation separately in this
paper. First, the asset return is accepted as given
when I explain why there is limited stock market
participation. Then limited stock market participation is accepted as given when I discuss its effect
on the asset return. Nevertheless, we ultimately
need to develop a general equilibrium framework
that explains both simultaneously, but this is
beyond the scope of this paper.
Limited stock market participation may have
large effects on asset prices. The risk of the stock
market return is measured by its covariance with
shareholders’ consumption growth in the standard
framework, the consumption-based Capital Asset
1
Hui Guo is an economist at the Federal Reserve Bank of St. Louis.
Bill Bock provided research assistance.
Mehra and Prescott (1985) argue for an equity-premium puzzle: the
observed equity premium is too large to be explained by existing
theories.
M AY / J U N E 2 0 0 1
37
REVIEW
Pricing Model. Mehra and Prescott (1985) calculate
this covariance using aggregate consumption data
and find that it is too small to explain the observed
equity premium unless we believe that investors
are extremely risk averse.2 This is the so-called
equity premium puzzle. In their calculation, Mehra
and Prescott assume that everyone holds stocks so
that they can use aggregate consumption instead
of shareholders’ consumption. This assumption is
inconsistent with the data because not everyone
holds stocks. Recent research finds that limited
stock market participation does help explain the
equity premium puzzle. Using the Panel Study of
Income Dynamics data, Mankiw and Zeldes (1991)
find that shareholders’ consumption is more
volatile and more positively correlated with stock
market returns than non-shareholders’ consumption. Brav and Geczy (1996) and Vissing-Jorgensen
(1998a) document a similar phenomenon using
the Consumption Expenditure Survey data. In contrast, Guo (2000) explores the connection between
limited stock market participation and asset prices
by calibrating a heterogeneous agent model in
which only one type of agent holds stocks. Under
reasonable parameterizations, the simulated data
match the first two moments of the risk-free rate
and the stock market return.
A related issue is whether the most recent bull
market is brought about by the increase in stock
market participation. According to the Survey of
Consumer Finance data, the stock market participation rate has increased from 31.7 percent in
1989 to 48.8 percent in 1998. However, stockholdings remain extremely concentrated. For example,
the wealthiest 10 percent of U.S. households
owned 85 percent of total stocks and mutual funds
in 1998, only slightly lower than the 86 percent
owned in 1989. Wolff (2000) also reports that the
participation rate drops sharply if small shareholders are excluded. Therefore, there is little
change in the concentration of stock ownership
and the most recent bull market is unlikely to be
explained by the increase in the stock market participation rate.3 Given that stock prices fluctuate
widely in historical data, the most recent run-up
in stock prices may be deviations from the trend.
Limited stock market participation might also
help reconcile some macroeconomic anomalies.
For example, although rich people own almost all
stocks, their consumption share is relatively small.
This explains why aggregate consumption is not
very responsive to the stock price fluctuation. How38
M AY / J U N E 2 0 0 1
ever, the effects of limited stock market participation on business cycles have not been fully
explored yet. Future research along this line should
improve our understanding of the economy.
The paper is organized as follows. I first
present some stylized facts and then use an overlapping-generations model to help explain limited
stock market participation and wealth inequality.
In the last section I discuss the implications for
asset prices.
SOME STYLIZED FACTS
In this section, I summarize some stylized
facts about financial markets and stock market
participation.
• The stock return is persistently higher than
the risk-free rate over long horizons.
• Very wealthy households own almost all
stocks and other investment assets.
• The share of wealth held by very wealthy
households is positively correlated with
stock prices.
• The intergenerational transfer is an important channel through which wealth
inequality is preserved over time.
Stocks Outperform Risk-Free Assets
Over Long Horizons
The stock return is much higher than the riskfree rate. During the period 1871-1998, the real
continuously compounding stock market return
was about 7.0 percent per year and the risk-free
rate was only 2.4 percent.4 The difference is
dramatically amplified by the compounding effect.
If you invested one dollar in large company stocks
at year-end 1925, you would have had $1,370.95
by year-end 1996. On the other hand, investments
of one dollar in short-term government bonds
grew to only $13.54 over the same period (Ibbotson
Associates, 1997).
2
See Kocherlakota (1996) for a survey of recent research on this
issue.
3
Heaton and Lucas (1999) make a similar argument. They also explore
some other explanations for the most recent stock price run-up.
4
These returns were calculated from the historical data constructed
by Robert Shiller, which is available from his homepage
<http://aida.econ.yale.edu/~shiller/>. The risk-free rate may be
overestimated because it is the return on primary commercial
paper in Shiller’s data. The annual real return on treasury bills is
0.6 percent for the period 1926-96 (Ibbotson Associates, 1997).
FEDERAL RESERVE BANK OF ST. LOUIS
Figure 1
Figure 2
Annualized Equity Premium Over a 30-Year
Horizon
Assets Held by the Richest 10 Percent,
1983-1998
Percent
0.12
Percent
91
Stocks and Mutual Funds
Scale
0.10
0.08
Other Assets
Scale
88
Investment
Assets
Scale
0.06
0.04
Percent
46
43
85
0.02
0.00
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
SOURCE: Shiller’s Data. See footnote 4.
Fama and French (1988), among many others,
also document a mean-reverting process in stock
prices. This is often interpreted as meaning that
stocks are not as risky in the long run as they are
in the short run. In Figure 1, I plot the annualized
equity premium over a 30-year horizon. For
example, the value corresponding to the year 1997
is the average equity premium over the period
1968-97. The equity premium over a long horizon
is always positive, even for periods that include
the 1929 stock market crash.
However, it is not my intention to advocate
stock market investing. First, Samuelson (1994),
among others, is skeptical about these finite
sample results. He argues that “if you adhere to
the dogma that stocks must beat bonds in the
long-enough run, there is no P/E level that the
market averages out to at which you will take in
sail. A Ponzi bubble is ever possible, and given
past psychologies of boom and bust, ever-higher
P/E ratios become a self-fulfilling prophecy…”
(Samuelson, 1994, p.19). Second, the stock
market could be very volatile in the short run, and
it is rational for people not to participate in stock
markets if their wealth is too little to absorb large
shocks to stock prices. Heaton and Lucas (2000)
argue that even wealthy households should hold
fewer stocks if they have significant proprietary
income.
82
1983
1986
1989
1992
1995
40
1998
SOURCE: Wolff (2000).
Very Wealthy Households Own Most
Stocks
Stocks are highly concentrated in the hands of
very wealthy households. In 1998, the richest 10
percent of U.S. households owned 85 percent of
total stocks and mutual funds. They also held most
other investment assets, including financial securities, trusts, business equity, and non-home real
estate. In fact, they owned 86 percent of total
investment assets. However, they had only 44 percent of the other assets, including principal
residence, deposits, life insurance, and pension
accounts. As shown in Figure 2, their shares of
stocks and mutual funds, total investment assets,
and other assets are relatively stable over the
period 1983-98.
Portfolio compositions are also quite different
between the very wealthy and the average households, as shown in Figure 3. The principal residence
is the most important asset for average U.S. households, which accounted for 65.9 percent of total
assets for the poorest 80 percent of U.S. households
in 1995. They also allocated 11.1 percent in liquid
assets, 8.5 percent in pension assets, and 12.2 percent in investment assets. Conversely, the richest 1
percent put 78.5 percent of their total wealth in
investment assets, 6.4 percent in their principal residence, 7.7 percent in liquid assets, and 4.7 percent
M AY / J U N E 2 0 0 1
39
REVIEW
Figure 3
Figure 4
Portfolio Compositions: Top 1% Richest and
Bottom 80% Poorest
Wealth Distribution and Detrended Stock
Prices
Percent
Percent
45
80
Bottom 80%
Wealth Share of Top 1%
Scale
Top 1%
60
U.S. Dollars
4
35
2
25
0
40
20
Detrended Stock Price
Scale
0
Principle
Residence
Liquid
Assets
Pension
Assets
Investment
Assets
SOURCE: Wolff (1998).
in pension assets. Therefore, very wealthy households have a larger share of risky assets in their
portfolios than average households do.
Wealth Distribution Over Time
Wolff (1995) finds that wealth inequality moves
closely with stock prices. His results are reproduced
in Figure 4, which plots the share of wealth held by
the richest 1 percent of U.S. households and the
detrended stock prices for the period 1922-98.
It is clear that these two variables move
together, with a correlation coefficient of about
0.61. There are two reasons. First, the wealthiest 1
percent own almost all stocks, whereas the
principle residence is the most important asset
held by the average U.S. households. Second, stock
prices are much more volatile than the prices of
other wealth components, including principle residence. Changes in the valuation of existing assets
are thus dominated by fluctuations in the stock
market (Ludvigson and Steindel, 1999).
Inheritance and Wealth Inequality
There are many factors that explain wealth
inequality, such as successful entrepreneurial
effort, life-cycle savings, precautionary savings, and
inheritance. Here, I want to stress the empirical relevance of inheritance, which is a key element of
the model presented in the next section.
40
M AY / J U N E 2 0 0 1
15
1920
1930
1940
1950
1960
1970
1980
1990
-2
2000
SOURCE: Wolff (1995) and Wolff (2000) for wealth share;
Shiller’s data for stock prices.
Inhaber and Carroll (1992) argue that
inheritance is one of the most important sources
of wealth for the richest people, while it is a minor
source of assets for most others. For example, 80
percent of the U.S. population claims never to
have inherited any assets, and only 1 percent of
the population admits to having inherited assets of
$110,000 or more (Inhaber and Carroll, 1992, p.
73). Moreover, in both 1988 and 1989, more than
one third of the 400 wealthiest Americans listed
their primary source of wealth as inheritance,
according to Forbes magazine.
Kotlikoff and Summers (1981) argue that intergenerational transfers account for the vast majority
of aggregate U.S. capital formation. Dynan, Skinner,
and Zeldes (2000) also find that inheritance is crucial in explaining the different saving pattern
between the rich and the poor.
A LIMITED STOCK MARKET
PARTICIPATION MODEL
For simplicity, I adopt an overlapping generation model with bequest motives, which is similar
to the model studied by Galor and Zeira (1993).
While Galor and Zeira emphasized the importance
of different education opportunities in explaining
wealth inequality, I assume that households have
different investment opportunities—they can
invest in either stocks or bonds. The stock return
FEDERAL RESERVE BANK OF ST. LOUIS
is higher than the bond return; however, there is a
fixed stock market entry cost. In the credit market,
banks accept deposits and make loans and the
household faces a borrowing rate that is higher
than the saving rate. I show that, initially, only
households with endowments over a certain
threshold find it optimal to hold stocks because of
the fixed entry cost and the wedge between the
saving rate and the borrowing rate. Moreover,
some households that initially hold stocks eventually leave the stock market because their relatively
small endowments do not allow them to leave a
large bequest. Rich households, however, always
hold stocks and accumulate wealth faster than
poor households do because they enjoy a higher
rate of return on their assets. As a result, wealth is
unequally distributed and rich people will hold all
stocks in the long run.
Model Setup
There is a continuum of households in an
economy that persists forever. At time t, each
household, say i, has a new cohort born, h ti. The
new cohort receives a bequest, M ti, from a parent,
h ti–1, which can be invested in stocks or bonds. At
time t+1, he receives labor income, L, and the
payoff from his earlier investments.5 After leaving
bequest Mti+1 to his one child, h ti+1, he consumes
the rest of his wealth and exits the economy.
It is costly to enforce loan contracts. Cohorts
can save or borrow only through banks, which have
the lowest enforcement costs. Banks raise money
by issuing bonds, which promise a gross rate of
return, Rb. I assume that the enforcement cost is
proportional to borrowers’ leverage ratio and a
cohort can borrow only at the rate Rb(1+D/W),
where D is his outstanding debt and W is his net
worth.6 A cohort can also invest in stocks, which
offer a higher rate of return, Rs, than bonds do.
However, there is a fixed stock market entry cost
F>0. The fixed entry cost can be interpreted as
informational costs and factors that affect stock
market participation decisions, as discussed in the
introduction.
For simplicity, I assume that the gross stock
return, Rs, and the gross bond return, Rb, are constant and that Rs>Rb in the baseline model.
However, adding noise to stock returns does not
change the results in any qualitative way as long
as stocks are better investments than bonds, in the
sense that mean returns to stocks are larger than
mean returns to bonds.
Lastly, there is a progressive tax, tb, on inheritance, which will be discussed in more detail in
the next section.
Maximization Problem
Since cohorts differ only in their endowments
and bequests, I ignore the superscript i and subscript t. Instead, I denote Mti, the bequests received
by a t-cohort, as M; and I denote Mti+1, the bequests
left by a t-cohort, as B.
Cohorts have identical preferences, which
depend on consumption, C, and bequests, B. The
utility function is
(1)
Max{C ,B} a log(C ) + (1 - a ) log[(1 - t b ) B],
where a is the relative weight given to consumption.
The maximization of equation (1) is subject to
budget constraints. If a cohort decides to stay out
of stock markets and invest all his endowments in
bonds, his budget constraints are
(2)
C + B £ MRb + L.
Otherwise, he chooses to pay the fixed entry cost
F to invest in stocks and his budget constraints
are
(3)
C + B £ ( M + D - F ) Rs + L - DRb (1 +
D
),
M-F
where D is the amount that he borrows from the
bond market and Rb[1+D/(M – F)] is the rate at
which he can borrow because his net wealth is
M – F after he pays the fixed cost.
The progressive estate tax is defined by equation (4), for which only bequests that exceed a
–
maximum level B are taxed:
(4)
tb =
0 if B £ B
t if B > B .
The maximization is done in two steps. A
cohort first maximizes his total wealth, We, which
is the sum of the payoff to the first period’s investments and labor income. He then decides how to
5
Galor and Zeira (1993) show that heterogeneity in labor income
leads to wealth inequality. Here I want to stress the importance of
heterogeneity in investment opportunities on wealth distribution.
To ensure a clear demonstration, I assume that all cohorts receive
the same labor income, L. Adding Galor and Zeira’s heterogeneous
labor income should not change these results in any qualitative
way.
6
In a general equilibrium setting, Bernanke, Gertler, and Gilchrist
(1999) show that the cost of external funds depends negatively on
firms’ net worth relative to the gross value of capital.
M AY / J U N E 2 0 0 1
41
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allocate total wealth between consumption and
bequests.
Maximization of Wealth. A cohort does one
of two things: either (i) invests all his endowments
in bonds and his second period total wealth is
given by
We = MRb + L ,
(5)
or (ii) pays the fixed entry cost, F, to participate in
the stock market and his second period total wealth
is given by
D
We = max{D} ( M + D - F ) Rs + L - DRb (1 +
)
M-F
(6)
( R - Rb )2 ( M - F )
= s
+ ( M - F ) Rs + L.
4 Rb
Because We is a linearly increasing function of
endowments, M, in equations (5) and (6), there
exists a threshold level of endowment,
M* =
( Rs + Rb )2
,
( Rs - Rb )( Rs + 3Rb )
at which the cohort is indifferent to these investment strategies. Moreover, he chooses to invest in
bonds if his endowment is below M* and chooses
to invest in stocks otherwise. The total wealth, We,
is then given by equation (7), which is a monotonically increasing function of M:
(7) We =
MRb + L,
if M £ M *
( Rs - Rb )2 ( M - F )
+ ( M - F ) Rs + L, if M ≥ M * .
4 Rb
Consumption and Bequests. A cohort chooses
consumption, C, and bequests, B, to maximize
equation (1), subject to the estate tax (equation (4))
and the reduced budget constraints (equation (8)):
C + B £ We ,
(8)
where We is defined by equation (7). Clearly, there
exist We* and We**, such that We*<We**, and the
optimal after-tax bequest is as follows:
(9)
(1 - a )We ,
if We £ We*
if We* £ We £ We**
B* =
B,
(1 - a )(1 - t )We , if We > We** .
Denote We–1 as the inverse function of We (M). It
is well defined because We (M) is a monotonically
42
M AY / J U N E 2 0 0 1
increasing function of M. Define M **=We–1(We*)
and M ***=We–1(We** ). It is clear that M *** is greater
than M **.
After substituting equation (7) into equation
(9), the optimal after-tax bequest is a function of
the endowment M as in equation (10). Here we
assume that M ** is greater than M * or that nonshareholders do not have to worry about the estate
tax.
(10)
B* =
(1 - a )( MRb + L), if M<M *;
(1 - a )[
( Rs - Rb )2 ( M - F )
+ ( M - F ) Rs + L],
4 Rb
if M * ≤ M < M **;
B, if M ** ≤ M ≤ M ***; and
( Rs - Rb )2 ( M - F )
+ ( M - F ) Rs + L],
4 Rb
otherwise.
(1 - a )(1 - t )[
The Dynamics of Wealth
Equation (10) is a first-order difference equation of bequests. The phase diagram is plotted in
Figure 5 under the following conditions:
1. (1 - a ) Rb < 1,
È ( Rs - Rb )2
˘
+ Rs ˙ > 1,
2. (1 - a ) Í
Î 4 Rb
˚
È ( Rs - Rb )2
˘
(
1
a
)(
1
t
)
+ Rs ˙ < 1.
3.
Í
Î 4 Rb
˚
The first condition ensures at least one stable
steady state, which is the same as in Galor and Zeira
(1993). The second condition ensures that the
wealth diverges in the long run. The third condition
is required so that rich people’s wealth has a welldefined steady state; otherwise, it goes to infinity.
These conditions hold under reasonable
parameterizations. Let us assume that there are 30
years in each period. According to Shiller’s data, Rb
is about 200 percent and Rs is about 760 percent.
The current highest marginal estate tax rate is 60
percent. Under this parameterization, conditions 1
FEDERAL RESERVE BANK OF ST. LOUIS
Figure 5
Figure 6
Dynamics of Bequest
Dynamics of Bequest with Uncertainty
B
B
State 1
State 2
ML
M* MM
M**
M***
H
M
M
through 3 hold for 0.78<a<0.91. If the population grows at 2 percent per year, conditions 1
through 3 hold for 0.60<a<0.84.7
The following results are shown clearly in
Figure 5. First, cohorts with initial endowments
less than M * do not hold stocks. Second, there are
two stable steady states M L and M H. Rich households with initial endowments greater than M M
converge to M H, and the remaining poor households converge to M L. Therefore, wealth is
unequally distributed and only rich people hold
stocks in the long run. Third, reductions in the
fixed entry cost move M * toward the origin and
therefore increase stock market participation.
Due to its simplicity, the baseline model does
not provide a complete description of data. In particular, the actual wealth distribution is not bimodal,
people do move up and down the economic scale,
and wealthy households also own a significant
amount of bonds in the data. This is because random factors are assumed away in the baseline
model. For example, entrepreneurial success or
failure can generate mobility in wealth; rich people
hold bonds to diversify risks. Incorporations of
these considerations should improve our model’s
prediction. As an example, I will show in the next
section of the article that our model can generate
a more realistic wealth distribution if stock returns
are stochastic.
Stochastic Stock Returns. For simplicity and
L
M
M
M
M2
M
H2 H
M
M
M
without loss of generality, I assume that stock
returns are random realizations of two values and
are not serially correlated. Also, the investment
decision is made before the stock return is realized. Each cohort now maximizes the expected
utility in his first period and his consumption/
bequest decision is the same as in the case of certainty. If conditions 1 through 3 hold in each state,
the dynamic of bequests with stochastic stock
returns is shown in Figure 6. Note, the portfolio
decision is independent from the state because
stock returns are not serially correlated.
In the long run, the households with initial
endowments less than M M converge to M L. The
households with initial endowments greater than
M M2 converge to the M H2/M H region. The other
households’ wealth may fluctuate between M M
and M M2, depending on the realizations of the
stock return. The stochastic return model thus
generates an additional middle class who owns
7
If there is a population growth, conditions 1 through 3 become as
follows (where Rp is the growth rate of population):
1. (1 - a ) Rb
1
< 1,
Rp
È ( Rs - Rb )2
˘ 1
+ Rs ˙
> 1,
Î 4 Rb
˚ Rp
2. (1 - a ) Í
È ( Rs - Rb )2
˘ 1
+ Rs ˙
< 1.
R
4
b
Î
˚ Rp
3. (1 - a )(1 - t ) Í
M AY / J U N E 2 0 0 1
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some stocks. Moreover, it predicts that the wealth
inequality, or the share of wealth held by the rich,
increases with stock prices, as observed in the data.
IMPLICATIONS FOR ASSET PRICES
The asset return is taken as given in the limited
stock market participation model presented in the
previous section. In this section, I discuss the
effect of limited stock market participation on the
asset return.
Agents are usually assumed to be homogeneous
in economic models for the sake of simplicity.
However, asset pricing models with homogeneous
agents do not provide a good description for the
asset return. They fail to explain why the equity
premium is so high (the equity premium puzzle)
and why the stock price is so volatile (the excess
volatility puzzle).
In modern asset pricing models, agents are
risk averse and prefer smooth consumption. The
return to an asset thus depends on how well it
can be used to smooth agents’ consumption. Intuitively, one dollar is more valuable in bad states
when consumption is low than one dollar in good
states when consumption is high. A stock is thus
unattractive, and shareholders demand a positive
premium to hold such a stock if its return is low
(high) when shareholders’ consumption is low
(high). Also, the more risk-averse shareholders are,
the larger the risk premium they require. It can be
shown that the equity premium is equal to gsr,s in
a frictionless economy, where g is a measure of
relative risk aversion and sr,s is the covariance
between the stock return and the shareholder’s
consumption growth. This is the so-called consumption-based Capital Asset Pricing Model.
Assuming that everyone holds stocks, Mehra and
Prescott (1985) calculate this covariance using
aggregate consumption and find (i) that it is too
small to explain the observed equity premium or
(ii) that there is an equity premium puzzle.
It also can be shown that asset price Pt is
equal to the sum of the expected cash flow, Dt+i,
weighted by stochastic discount factor Rt+i or
•
Pt = Et [ Â Rt + i Dt + i ] .
i =1
In the homogeneous agent model, Rt+i is equal to
the intertemporal marginal rate of substitution
bi
44
M AY / J U N E 2 0 0 1
U (Ct + i )¢ ,
U (Ct )¢
where b is the time discount factor, U ´ is the marginal utility, and Ct is the aggregate consumption
at time t. Variations in asset prices thus come from
two sources: shocks to the cash flow, Dt+i , and
shocks to the stochastic discount factor, Rt+i , which
in turn are caused by aggregate consumption
shocks. Shiller (1981) finds that dividends are too
smooth to explain many variations in stock prices.
Similarly, Campbell (1991) finds that most variations in stock prices come from innovations in
the stochastic discount factor. However, the aggregate consumption is too smooth to generate the
volatile stochastic discount factor implied by the
financial data. This is the excess volatility puzzle.
The preference is assumed to be time separable
in the examples given above. Constantinides (1990)
shows that it is possible to use aggregate consumption to generate a volatile stochastic discount factor
as well as a large and volatile equity premium in a
habit formation model, in which utility depends on
both current and past consumption. However, the
risk-free rate is very volatile in his model because it
is also priced by the same volatile stochastic discount factor. This is easy to understand. Cash flows
are stochastic on stocks and are predetermined on
bonds. Given that dividends are smooth in the data,
this difference is rather small. Stocks and bonds
should thus exhibit similar properties, i.e. means
and/or variance if they are priced by the same
stochastic discount factor. However, the stock return
is much higher and much more volatile than the
bond return in the data. Therefore, stocks and bonds
are not likely to be priced by the same stochastic
discount factor. This poses a serious challenge to
the homogeneous agent model.8
Recent research by Guo (2000) suggests that
these puzzles might be related to the fact that only
a few wealthy people own almost all stocks. He
shows that a heterogeneous agent model of
limited stock market participation can replicate
these phenomena.
There are two types of agents in his model:
one is a shareholder and the other is a nonshareholder. They both receive labor incomes, but
only the shareholder receives dividends. Labor incomes and dividends follow stochastic processes.
Both agents use bonds to diversify the income risk;
8
One exception is Campbell and Cochrane (1999), who avoid this
problem by choosing a particular habit form so that the risk-free
rate is constant. However, they need a very large risk-aversion to
explain the puzzles mentioned above. Therefore, they do not really
solve the equity premium puzzle.
FEDERAL RESERVE BANK OF ST. LOUIS
for example, an agent buys (sells) bonds when his
income is above (below) the trend. However, they
can borrow from the bond market only up to a
limited amount or there are borrowing constraints.
The model is calibrated using income processes
estimated by Heaton and Lucas (1996), and the
simulated data match the mean and variance of
stock returns and bond returns under reasonable
parameterizations.
Unlike the homogeneous agent model, stocks
and bonds may be priced by different stochastic
discount factors, as in the model by Guo (2000),
because of limited stock market participation.
In particular, while bonds are priced by the intertemporal marginal rate of substitution of a nonconstrained agent(s), stocks are always priced by
the shareholder’s intertemporal marginal rate of
substitution. Stocks and bonds are thus priced by
different intertemporal marginal rates of substitution when the shareholder’s borrowing constraints
are binding. The stochastic discount factor for
bonds is
max {b i
U (Cts+ i )¢ i U (Ctn+ i )¢
,b
}
U (Cts )¢
U (Ctn )¢
and for stocks it is
bi
Cts
Ctn
U (Cts+ i )¢ ,
U (Cts )¢
are consumption of the sharewhere and
holder and the non-shareholder, respectively. It is
clear that the former is larger and smoother than
the latter because borrowing constraints put a
lower bound on the discount factor of bonds, but
not stocks. Therefore, in Guo’s model, the bond
return is low and smooth while the stock return is
high and volatile, as observed in the data.
Intuitively, bonds are desirable and are priced
at a premium because they can be used to
diversify income shocks. Stocks are not desirable
because they cannot be used to diversify income
shocks. The precautionary saving motive thus
lowers only the risk-free rate, but not the stock
return. This echoes Weil’s (1989) argument that
the equity premium puzzle is indeed a risk-free
rate puzzle. To see this, the equity premium in Guo
(2000) is equal to gsr,s+rsf–min{rsf,rnf}, where g is
the relative risk aversion coefficient, sr,s is the
covariance between shareholder’s consumption
and the stock return, and rsf (rnf ) is the shadow
risk-free rate of the shareholder (non-shareholder).
The equity premium is larger in Guo’s model than
in the representative agent model for two reasons.
First, there is an extra nonnegative term,
rsf–min{rsf,rnf}, reflecting the fact that bonds
(stocks) can (cannot) be used to hedge income
shocks. This term can be interpreted as a liquidity
premium. Second, the covariance between shareholders’ consumption and the stock return is
larger than the covariance between aggregate consumption and the stock return.
CONCLUSION
The recent Survey of Consumer Finance data
show that stocks are highly concentrated in the
hands of a few wealthy people. In this paper, I
used an overlapping-generations model to help
explain this limited stock market participation and
discussed its effect on asset prices. However, other
implications, such as its effect on business cycles,
have not been fully explored yet. Future research
along this direction should improve our understanding of the economy.
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