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The News in
Financial Asset Returns
GERALD P. DWYER JR. AND CESARE ROBOTTI
Dwyer is the vice president in charge of the financial section and Robotti is a
financial economist and assistant policy adviser, both in the Atlanta Fed’s research
department. The authors thank Thomas Cunningham, Mark Fisher, Paula Tkac, and
Daniel Waggoner for helpful comments on earlier drafts. They also thank Budina
Naydenova and Shalini Patel for outstanding research assistance.

re returns on financial markets useful
for predicting the future course of the
economy? It is widely thought that
financial markets’ movements reflect
the economy’s future and that finding
the message in financial asset returns
is one way to discern this future. The message is not
always clear, though. For example, on November 3,
2003, a Wall Street Journal story attempted to reconcile apparently conflicting signals from stock and
bond prices about whether economic growth would
continue to be high in the future (Browning and
Lucchetti 2003).
The widespread notion that financial asset
returns are related to future economic activity is
plausible. An improvement in a company’s prospects
is likely to result in a rise in its stock prices
(Kamstra 2003). If a widespread increase in stock
prices occurs, it is possible that many companies’
prospects have improved and the economy will
grow faster. Those brighter prospects can be associated with faster output growth simply because
increases in asset prices reflect good news about
future economic conditions. Rising stock prices also
can be associated with faster output growth
because higher stock prices increase households’
wealth, thus boosting consumption and output and
thereby improving firms’ prospects. Either way, rising stock prices can be associated with future
increases in output.

Returns on bonds also can reflect the economy’s
future although the relationship is slightly more
complex. The actual return on a bond in any period
includes both an expected and an unexpected
return. By definition, the expected return is not a
surprise and is not news. The unexpected return,
on the other hand, reflects news about the future.
Higher growth lowers bond prices and therefore
returns; lower growth raises bond prices and therefore returns.1
Detailed analyses provide surprisingly little support for financial markets’ ability to reveal future
economic activity. For example, Stock et al. (1989)
find that aggregate stock returns have little value
for predicting economic activity, given other variables. They emphasized new leading indicators such
as term and default spreads, which promptly failed
to forecast a recession (Stock and Watson 2003).
The evidence on alternative indicators based on asset
prices is mixed (Smith 1999). In part, the problem is
that a variety of indicators based on different, seemingly plausible lines of argument have been proposed.
Typically, researchers who propose an indicator find
the data consistent with its importance, and then
other researchers who test the indicator find the evidence lacking (Stock and Watson 2003).
Despite the less than sterling record associated
with such indicators, there is a strong temptation to
use movements in stock indexes and more general
returns on financial markets to help discern the

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE
Stock Prices and Recessions, January 1947 through December 2003

S&P 500

1,000

100

10
1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Source: S&P 500 from CRSP

future of the economy. After all, other forecasts are
not particularly helpful either. Stock prices reflect
investors’ expectations of the future, and it is hard to
imagine that they do not contain useful information.2
Even if stock returns do help predict future economic activity, the level of stock prices measured by
an index number is not necessarily the best way to
use asset prices to forecast the economy. Lamont
(2001) provides evidence that the market stock
portfolio is not the best portfolio for predicting
future economic activity. Creating a market portfolio based on firms’ market values can bury industryspecific data that might be informative about
future economic activity such as output growth and
inflation. As a consequence, Lamont investigates
predictions of economic activity from alternative
combinations of the information contained in asset
prices, which he calls economic tracking portfolios—
portfolios of stocks and bonds with returns that have
the best-fitting joint linear relationship with economic activity.3 Lamont (2001) finds that tracking
portfolios are related to future economic activity and
presents evidence that they are useful for forecasting economic activity over the next year.
Curiously, there is more solid evidence that financial markets reflect news about economic activity
than that markets’ reflection of news helps predict
2

economic activity. Two early papers on the effect on
financial markets of news about economic activity are
Chen, Roll, and Ross (1986) and Dwyer and Hafer
(1989), and two recent papers are Flannery and
Protopapadakis (2002) and Balduzzi, Elton, and
Green (2001). Overall, these studies do find evidence
of relationships between financial asset returns and
economic activity although the relationship is more
evident for bonds than for stocks.
This article examines and answers two questions:
First, what is a good way of extracting information
about future economic activity from asset prices?
Second, do financial asset returns help predict economic activity over horizons from one month to five
years? While the questions are similar, in part, to
Lamont’s (2001), the methods used here are different
in some regards, and more recent data allow us to
examine the late 1990s and the recession in 2001.

Stock Returns and Recessions
efore delving into evidence from a more technical
analysis, we present some simple evidence on a
basic question: How well do stock prices predict
recessions? The figure above shows the S&P 500
stock index from January 1947 to December 2003; the
shading represents periods of recession as defined by
the National Bureau of Economic Research (NBER).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 1
Recessions and Stock Returns

Recessions
(years)
1948–49
1953–54
1957–58
1960–61
1969–70
1973–75
1980
1981–82
1990–91
2001
Average

Peak of
stock
index (1)
(month)

Peak of
business
cycle (2)
(month)

06/48
12/52
07/57
07/59
11/68
12/72
01/80
11/80
05/90
08/00

11/48
07/53
08/57
04/60
12/69
11/73
01/80
07/81
07/90
03/01

Lead
Stock index
time
decline
between
from
peaks
(1) to (2)
(months)
(percent)
5
7
1
9
13
11
0
8
2
7
6.3

–11.95
–6.83
–5.62
–10.15
–15.05
–17.37
0
–6.83
–1.40
–23.55
–9.88

The figure makes it easy to see why it is tempting to
use a stock market index to predict recessions. Every
recession is associated with a fall in the S&P 500, with
all but the drop in 1980 preceding the recession.
Table 1 summarizes details from the figure. The
analysis is similar to Siegel’s (1998, chap. 12) analysis of decreases in the S&P 500 index before recessions.4 Table 1 shows whether the stock market falls
before the beginning of a recession and starts to rise
before the end of a recession. Stock prices can rise
and fall on successive days, so it would be meaningless to simply examine whether stock prices fall
before a recession. On some days they do; on some
days they don’t. A more informative definition is
Siegel’s; he defines a fall before a recession as a
decline of 8 percent or more and a peak as the highest level from which prices fall 8 percent. The low
point of the index—a trough—is the lowest level

Trough
of the
stock
index
(month)

Maximum
decline
of stock
index
(percent)

06/49
08/53
12/57
10/60
06/70
09/74
03/80
07/82
10/90
09/01

–15.47
–12.23
–16.53
–11.77
–32.90
–46.18
–10.57
–23.79
–15.84
–31.41
–21.67

Lead time
from trough
to the end
Length
End of the
of the
of the
recession
recession recession
(month)
(months)
(months)
10/49
05/54
04/58
02/61
11/70
03/75
07/80
11/82
03/91
11/01

5
10
5
5
6
7
5
5
6
3
5.7

12
11
9
11
12
17
7
17
9
9
11.4

before stock prices rise 8 percent. Table 1 also
examines whether a stock market increase signals
the end of the recession; we use a related definition
of such a signal as being a rise of 8 percent or more
that started during the recession.
Table 1 shows that stock prices peak anywhere
from zero to thirteen months before the start of a
recession. The average lead time between the peak
of the S&P 500 and the start of a recession is 6.3
months, and the average stock market decline
before a recession is 9.9 percent. The average
decline in the stock market before and during a
recession is 21.7 percent, with a wide range from a
10.6 percent fall in 1953–54 to the collapse of stock
prices by 46.2 percent in 1973–75. On average,
these declines took place over twelve months.
Typically, the stock market falls less before a recession than during it, which limits the market’s value

1. In general, higher growth is associated with a higher expected return. News of higher growth in the future, though, is associated with a lower unexpected return today because the price of a fixed-income security must fall to provide higher expected
returns in the future.
2. See, for example, Del Negro (2001), Smith (1999), and Stock et al. (1989). The basic idea is related to Hayek (1948).
3. Economic tracking portfolios are similar to “maximum correlation portfolios,” introduced by Breeden, Gibbons, and
Litzenberger (1989), and “mimicking portfolios,” which have been used for a variety of purposes, including tests of asset pricing models. For example, Breeden, Gibbons, and Litzenberger (1989) construct tracking portfolios for current consumption
to test the consumption capital asset pricing model, and Balduzzi and Robotti (2001) use tracking portfolios to test the
intertemporal capital asset pricing model. Returns on such portfolios also can be used to calculate the risk premia received
by holders of various types of risk. In fact, the economic risk premia are the excess cash flows on the mimicking portfolios
(Robotti 2002). Such portfolios can be used as hedging devices by individuals who wish to insure themselves against a particular economic risk; for example, to insure themselves against inflation, individuals can take a position in the mimicking
portfolio for inflation to offset predictable inflation.
4. The NBER recession dates—other than the 2001 recession, which occurred after Siegel’s analysis was published—are identical. The dates and stock market returns are similar but differ at least partly because Siegel appears to have used monthly
averages of the S&P 500 index and this study uses the value at the end of the last trading day of the month.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

for forecasting recessions. Still, decreases in the
stock market appear to be useful for identifying
whether the economy is currently in a recession.
Given the typical ups and downs of the economy,
there have been many times in the last fifty years
when it has been difficult to determine that the
economy is in a recession even while one is under
way—a difficulty even in the two most recent recessions. The stock market does not necessarily
decline substantially before a recession, but the
onset of a recession is invariably associated with a
substantial decline in stock prices.
In every case, prices have begun to increase
before the end of a recession. The figure shows this

It is widely thought that financial markets’
movements reflect the economy’s future and
that finding the message in financial asset
returns is one way to discern this future.

pattern, with stock prices starting to increase 5.7
months, on average, before the end of the recession.
This analysis answers part—but not all—of the
question about stock prices’ ability to predict recessions. The analysis shows that if the NBER identifies a recession, then a fall in stock prices has
occurred about the same time in every recession
since World War II.
These results do not imply that substantial
decreases in stock prices indicate that there is a
recession. Falling stock prices are not a certain
indicator of a recession, as the patterns for 2001 to
early 2003 show. The S&P 500 index fell 29.0 percent from December 2001 to September 2002, rose
14.9 percent from September to November 2002,
and then fell 10.2 percent from November 2002 to
February 2003. There was no recession within
twelve months of the start of the 2002–03 decreases
in stock prices. In fact, the revised estimate of GDP
growth for the third quarter of 2003 is growth at
more than an 8 percent annual rate—more than a
little distant from a recession. Table 2 presents the
other episodes since World War II in which stock
prices fell 8 percent or more and no recession began
within twelve months after the fall began. Even
though they are associated with recessions, falling
stock prices do not necessarily mean that a recession is coming or is under way.
4

In the post–World War II period, an 8 percent
drop in stock prices has signaled nineteen recessions—nearly twice as many as the ten that have in
fact occurred.5 Recessions have occurred only 53
percent of the time that falling stock prices would
suggest a recession, indicating that falling stock
prices are roughly a fifty-fifty predictor of recessions. This statement is not the same as saying that
falling stock prices would predict a recession 50
percent of the time. Fortunately, an 8 percent drop
in stock prices is not that common. If stock prices
drop by 8 percent or more, there is about a 50 percent chance of a recession. Given that falling stock
prices do appear to be a signal about the economy’s
prospects, is there a way to extract more general
information about the economy from stock prices
and other financial asset returns?

Asset Returns and News about
Economic Activity
his section outlines a way to extract the news
about future economic activity from returns on
financial assets. The unexpected part—by definition, the part that is news—of an asset’s excess
return can reveal information about unexpected
economic activity.6 Let εt+1 be the unexpected part
of economic activity from t to t +1 and ηt be the
unexpected part of a financial asset’s excess return
from t –1 to t. The linear relationship between the
unexpected part of economic activity and the news
in the asset’s return is given by

(1) εt+1 = bηt + et+1,
where bη t is the part of the next period’s economic
activity predicted by the current news in the asset’s
excess return, ηt, and et+1 is the part of economic activity not predicted by the news in the asset’s return.
There are no data series called “unexpected economic activity” and “news in financial returns.” To
determine εt+1 and η t, we must estimate expected
economic activity and assets’ expected excess
returns because the unexpected parts of economic
activity and returns are the differences between
actual values and expected values. Let yt+1 denote a
measure of economic activity such as the growth
rate of industrial production from period t to t +1,
and let rt denote the excess return on an asset from
the end of period t –1 to the end of period t. The
unexpected part of the variation of economic activity and of the excess return can then be written as
(2) ε t+1 = yt+1 − E[ yt+1 | Ωt−1 ]
ηt = rt − E[ rt | Ωt−1],

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 2
Years of False Alarm
False
alarms
(years)

Peak of
stock index
(month)

Trough of
stock index
(month)

Maximum
decline of
stock index
(percent)

1956–57
1962
1966
1978
1984
1987
1998
2001–02
2002–03

07/56
12/61
01/66
08/78
11/83
08/87
06/98
12/01
11/02

02/57
06/62
09/66
10/78
05/84
11/87
08/98
09/02
02/03

–12.42
–23.48
–17.57
–9.82
–9.53
–30.17
–15.57
–28.99
–10.16

where Ωt–1 is the set of information available at the
end of period t –1, E[yt+1 Ωt–1] is the expected level
of economic activity conditional on information
available at the end of period t –1, and E[rt Ωt–1] is
the expected return in period t conditional on information available at the end of period t –1.
Only the unexpected part of economic activity is
related to news in the asset’s return because the
information already known about economic activity
is reflected in the expected part of economic activity and the asset’s expected excess return. These
unexpected parts of economic activity and the
asset’s return can be related to actual economic
activity and the actual return on an asset by linear
regressions.7 The news in the asset’s return about
future economic activity is uncorrelated with the
part of economic activity not predicted by such
news by construction.
This simple relationship easily can be extended
to include more assets and cover more periods. If
the unexpected return on asset i is ηi,t and there
are N assets, then the relationship between unexpected growth of economic activity and the unexpected returns on the N assets is
(3) εt+1 = b1η1,t + b2η2,t + … + bNηN,t + et+1.

It is useful to extend economic activity in equation
(1) to cover several periods instead of one period
because the unexpected return on an asset in any given
month generally reflects information about more than
one month. For an asset such as stock or a bond with a
life longer than one period, the unexpected return on
the asset in period t reflects changes in expectations not
just for this period but for all future periods reflected
in the asset’s price. The unexpected return is part of the
capital gain portion of the asset’s total dollar return—
the change in the asset’s price—and the unexpected
part of the change in this price reflects changes in the
payoffs to investors in any or all of the periods over the
entire life of the asset. This version of equation (1) over
a longer horizon for economic activity is
(4) ε kt+1 = bηt + etk+1 ,
where e kt+1 is the part of the growth rate of economic
activity from t to t + k that is not predicted by the
news in asset prices in period t and the superscript
indicates the number of periods for which growth
rates are computed. The error term in equation (4)
is serially correlated in general if the data are sampled every period (that is, at t+1, t+ 2,. . .) and unexpected economic activity overlaps.8 This serial

5. This finding is similar to the findings of Samuelson (1966) and Siegel (1998, chap. 12).
6. The excess return on an asset is defined as the return on that asset minus the return on a riskless security.
7. We use the notation of mathematical expectations and call the measures “expected,” but linear projections are sufficient for
our purposes.
8. This overlap induces a moving-average error term with k nonzero autocorrelations. This autocorrelation is consistent with the
definition of news and unexpected economic activity. News (ηt, ηt+1,…) is serially uncorrelated. Unexpected economic activity
for one period (ε1t+1, ε1t+2,…) has one moving-average term because expected economic activity from t to t+1 is conditioned on
information in t–1. Unexpected economic activity for two periods (ε 2t+1, ε 2t+2,…) has two moving-average error terms, and so on.
If the underlying relationship between news in asset returns and economic activity is exactly linear, then equation (1) with
standard forecast updating and equation (4) yield identical forecasts with minimum mean-squared error.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

correlation complicates estimation of equation (4)
but does not preclude the usefulness of the oneperiod unexpected return’s information about economic activity over several future periods.
Identifying unexpected economic activity and
the unexpected excess return is more problematic
because neither is directly observable. To identify
ε kt+1 and η j, t( j = 1,..., N ), equation (2) shows that it
is necessary to estimate expected economic activity
and expected excess returns on the assets because
the unexpected parts of economic activity and returns
are the differences between actual and expected
values. With multiple periods and assets, equation
(2) becomes
k
k
k
(5) ε t +1 = yt +1 − E[ yt +1 | Ωt−1]

ηi,t = ri,t − E [ ri,t | Ωt−1],

where y kt+1 is the growth rate of economic activity
from t to t + k and ri,t is the return on asset i in period
t. If there is a linear relationship between expected
economic activity, the expected excess returns
on assets, and other variables known to investors
(zj,t–1, j = 1,…, M ), expected economic activity and
expected excess returns are given by
(6) E[ ytk+1 | Ωt−1 ] = α y + β1, y z1, t −1 + β2, y z2, t −1
+ ... + βM, y z M, t −1.
(7) E[ri, t | Ωt −1 ] = α r + β1,r z1, t −1 + β2,r z2, t −1
+ ... + β M, r zM, t −1.
Including the same variables in both equations
may seem restrictive, but it is not because some
coefficients in equations (6) and (7) can be zero.
It is more restrictive to limit the analysis to a linear relationship. This limitation can be justified
by assuming that variables are jointly normally
distributed, but this assumption is implausible.
Even without the assumption of normal distributions, the econometric analysis and conclusions
are correct if they are limited to linear relationships—that is, if the analysis is limited to the
information in the linear relationships among
variables. Equations (6) and (7) raise another
important issue, though. There is no reason to
think that the set of variables used in equations
(6) and (7) is the complete set of information
known to investors.
What are the consequences of not knowing all
the information available to investors? The implications can be illustrated with one asset. Using equations (6) and (7), we can rewrite equation (4) as
6

(8) ytk+1 − (α y+ β1, y z1, t −1 + β2, y z2, t −1 + ... + β M, y zM, t −1 )
= b[rt − (α r + β1,r z1, t −1 + β2, r z2, t −1 + ...
+ β M, r zM, t −1)] + etk+1 ,
which can be simplified to
k
(9) yt +1 = ( α y − bα r ) + brt + (β1, y − β1, rb)z1, t −1

+ (β2, y − β2, r b)z2, t −1 + ...
+(β M, y − β M, rb)z M, t−1+ etk+1.
The coefficients in equation (9) can be summarized
for convenience by
k
(10) yt +1 = γ + brt + δ1 z1,t −1 + δ 2 z2, t −1

+ ...δ M z M, t −1 + etk+1.
If only a subset of the information available to
investors—say, z1,t–1—is included in (10), the estimated equation is
(11) ytk+1 = c0 + c1rt + dz1,t −1 + vtk+1
instead of equation (10). The estimated relationship between unexpected economic activity and the
unexpected excess return, measured by the coefficient c1, will be the same as b if the excess return is
uncorrelated with the variables left out of the estimated equation. If the variables included in equation (10) but left out of (11) are correlated with the
excess return, then the estimated coefficient c1will
not be the same as b. In general, this source of
bias is not likely to be empirically important in our
analysis because we use monthly data on excess
returns, and excess returns on stocks and bonds are
not very predictable at this frequency. It might
seem that we could lessen the likelihood of this bias
by including numerous, possibly superfluous, variables, but obtaining an excellent fit in sample and a
worse fit out of sample is a likely and often serious
consequence of this strategy. Given that the purpose of the analysis is to forecast economic activity,
we limit the analysis to a relatively small set of variables to lessen problems of overfitting.
Even if variables left out of the estimated equation do not help predict the excess return, they
might help predict economic activity, in which case
the estimated error term, v kt+1, in equation (11) is
bigger than the underlying error term, e kt+1. If so, we
are more likely to find that the relationship between
economic activity and the excess return is statisti-

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

cally insignificant and therefore conclude that the
excess return provides little information about economic activity even if there is such a relationship.
The remainder of this article focuses on estimates of the following equation:
k
(12) yt +1 = c0 + c1 r1,t + c2r2,t + ... + cN rN, t
+ d1 z1, t−1 + d2 z2, t−1 + ...

+ dM zM, t−1 + ς tk+1 .
For each measure of economic activity, y, with N
financial assets and M additional variables, we study
the properties of the “economic tracking portfolio,”
c1r1+c2r2…+cNrN. Equation (12) can be estimated
by ordinary least squares (OLS), and we do so. The
standard errors and test statistics reported in the
article are based on the Newey and West (1987)
correction of estimated standard errors with twelve
lags; this method corrects for the serial correlation
in ζ kt+1 caused by economic activity being measured
over overlapping periods.

by risk factors for returns (Veronesi and Santos
2001)—and the unemployment rate. The inflation
rate is based on the consumer price index. Measures
of future financial economic activity included are the
excess return on the Center for Research in Security
Prices (CRSP) value-weighted aggregate portfolio
over the horizon, the excess return on a portfolio
of long-term government bonds (with a term of
approximately twenty years), and the return on onemonth Treasury bills.
The analysis uses growth rates for all but two of
the variables; the analysis uses the change in the
unemployment rate and financial assets’ excess
returns themselves. Growth rates better represent

Rising stock prices can be associated with
faster output growth because higher stock
prices increase households’ wealth, thus
boosting consumption and output and
thereby improving firms’ prospects.

The Data
or asset returns and economic activity, the period
covered by the data in this article generally is
February 1947 to August 2002, and for other variables, January 1947 to July 2002.9 These starting
and ending dates are dictated by data availability
and the end of World War II. All series are monthly.
Economic activity. The measures of aggregate
economic activity examined include industrial production, consumption, labor market activity, inflation, and future returns on financial markets.
Industrial production is measured by total industrial
production and broad production classes: manufacturing, consumer durable goods, consumer nondurable goods, mining, and utilities. Production of
durable goods has more cyclical variation than the
other classes of production, so it is worthwhile to
examine durable goods separately from nondurable
goods and manufacturing. Consumption is measured
by total consumption and two components: consumption of durable goods and consumption of nondurable goods and services. Labor market activity is
measured by real labor income—a variable suggested

the short-run variation in the series, which we are
trying to predict, instead of long-term trends. The
change in the unemployment rate filters out longterm trends in the level, and the excess returns
themselves do not have long-term trends, so it is
unnecessary, and undesirable, to use changes in
returns.
Returns. In the base set of regressions, returns
are measured by one aggregate stock index, eight
industry stock portfolios, and four bond returns. All
excess returns are one-month returns in excess of
the one-month Treasury bill return. The aggregate
stock index is the NYSE-AMEX-Nasdaq valueweighted stock market portfolio (from CRSP). The
industry indexes are for basic industries, capital
goods, construction, consumer goods, energy,
finance, transportation, and utilities.10 These industries are partly related to the component industries
in industrial production but are far from a one-toone mapping. The four bond returns are returns on

9. The change in the unemployment rate starts in January 1948 and ends in August 2002. The growth rate of real consumption starts in February 1959 and ends in August 2002.
10. The data appendix provides a more detailed description of the industries. We also conducted the empirical analysis with the
five Fama-French industries, which did not affect the conclusions. The five industry returns are for manufacturing, utilities,
shops, finance, and a catch-all category called “other industries.” These other industries include agriculture, mining, oil, construction, telecommunications, health services, and legal services. Again, further details on the definitions of the industries
are presented in the data appendix.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

a long-term government bond, an intermediate-term
government bond, a one-year government bond, and
a high-grade corporate bond. The long-term, intermediate-term, and high-grade bond returns are from
Ibbotson Associates. The one-year bond return and
the one-month Treasury bill rate are from CRSP.
Additional variables. The estimates of the
expected values of economic activity and returns
are based on regressions that include a constant
term and past values of eight variables that have
been used in tests of asset pricing models and studies of stock and bond return predictability (including, among others, Chen, Roll, and Ross 1986;
Burmeister and McElroy 1988; Ferson and Harvey

Creating a market portfolio based on firms’
market values can bury industry-specific
data that might be informative about future
economic activity such as output growth
and inflation.

1991, 1999; Downs and Snow 1994; Kirby 1998;
Balduzzi and Robotti 2001; and Lamont 2001). All of
these additional variables are the same in every
estimated equation.11 Three of the variables are
measured over the prior twelve months, and the
rest are measured over the prior month. The difference in the time frames is suggested by prior evidence of differences in the apparent persistence of
variables’ effects. The variables measured over the
prior twelve months, which are assumed to have
more persistent effects, are industrial production
growth, the inflation rate, and the aggregate excess
return on the CRSP value-weighted stock index.
The variables measured over the prior month,
which are assumed to have less persistent effects,
are the dividend yield, term premia for one-year
Treasury securities and long-term government
bonds relative to the one-month Treasury bill yield,
default premia measured by the commercial paper
yield minus the Treasury bill yield and the BAA
bond yield minus the yield on AAA corporate bonds,
and the return on a one-month Treasury bill itself.

The Evidence
n this section, we examine the importance of
news in financial market returns for future economic activity. We investigate whether the estimated
tracking portfolios are related to otherwise unpre-

dicted economic activity, and we estimate rolling
regressions to assess the out-of-sample performance
of the tracking portfolios in forecasting future economic activity.
The statistical significance of news in excess
returns. Is the news in asset returns related to
unexpected changes in economic activity? Table 3
summarizes the evidence by presenting p-values of
tests whether all of the thirteen excess returns are
related to the measures of unexpected economic
activity at horizons from one month to twelve
months.12 These p-values are the probability of a test
statistic as large as the one observed if the coefficients in equation (12) are zero. A large p-value
means that the test statistic is quite likely if the
restrictions are correct, and a small p-value means
that the test statistic is unlikely if the restrictions are
correct. A small p-value provides more support for a
relationship between the excess return and the measure of economic activity, with a p-value of 0.05 or
less being fairly unlikely if the variables do not belong
in the regression. Hence, we use the conventional pvalue of 0.05, or 5 percent, for deciding whether
news in financial asset returns is statistically related
to a measure of economic activity.13 These tests are
not independent, most obviously for components of
an aggregate; hence, we examine the results for
broad patterns and ignore occasional inexplicable
“statistically significant” results. For each measure of
economic activity and for each time horizon, Table 3
presents p-values for excluding all returns.
The tests show that news in financial assets’
excess returns is related to unexpected economic
activity. The p-values in Table 3 indicate that the
news in financial returns is related to total industrial
production, production of manufacturing goods,
mining, and production of consumer durable
goods—all cyclically sensitive—up to a six-month
horizon. Production of consumer nondurable goods
and of utilities are not related to the excess returns.
This result is consistent with the permanent income
theory of consumption, which implies that nondurable goods will be little affected by temporary
changes in income. The general relationship
between the news in financial asset returns and
consumption of durables goods, and the general
lack of such a relationship for consumption of nondurables and services, can be explained in a similar
way. The only p-value of 5 percent or less for real
labor income is at a horizon of twelve months. News
in financial returns is related to changes in the
unemployment rate and the inflation rate at all horizons. At all horizons, future excess returns are foreshadowed by news in financial returns.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 3
Economic Activity and All Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

P-values for Excluding All Returns from Basic Equation
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.02
0.03
0.57
0.48
0.13
0.07

0.00
0.00
0.06
0.03
0.72
0.56

0.00
0.00
0.03
0.03
0.65
0.38

0.22
0.20
0.08
0.25
0.65
0.88

Consumption
Total
Durable goods
Nondurable goods and services

0.17
0.24
0.25

0.00
0.00
0.05

0.20
0.00
0.31

0.06
0.00
0.14

Labor market
Unemployment rate
Real labor income

0.02
0.19

0.00
0.66

0.00
0.30

0.00
0.04

Inflation rate

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.01
0.00
0.00

0.03
0.00
0.00

0.00
0.00
0.00

0.01
0.00
0.00

Both stock and bond returns are related to economic activity. Table 4 shows the p-values for deleting the stock returns and for separately deleting the
bond returns.14 The low p-values show that news in
the market stock return and the eight industry portfolio returns are related to economic activity, with
the strongest relationship at the three-month and
six-month horizons. There is little relationship
between returns and industrial production and consumption one month in the future but more of a
relationship in the next three to six months. This
result is not necessarily surprising: It is plausible
that news about longer-term developments has
larger effects on these securities’ returns.15
Stock returns are represented in the regressions by the return on the aggregate market port-

folio and by returns on industry portfolios. Taken
together, the p-values in Table 4 indicate that the
combination of these returns is related to both
total and manufacturing industrial production
over the next three to six months. News in stock
returns is related to unexpected changes in both
unemployment and inflation at all horizons.
Interestingly, little evidence exists that stock
returns are related to unexpected changes in consumption, a surprising result given all the emphasis put on the “wealth effect”—a relationship
between wealth in corporate stock and consumption. News in stock returns appears to be related
to unexpected changes in the future financial
returns on bonds at all horizons and on stocks at
horizons of six months and more.

11. This strategy can be contrasted with a strategy of estimating autoregressions for the expected returns and measures of economic activity, which would include different variables in every equation and would run a risk of overfitting in sample and
quite possibly fitting worse out of sample.
12. In addition to one to twelve months, we also examined the ability of returns to forecast economic activity five years ahead.
There is little evidence that returns help to predict activity over this longer horizon.
13. This method is not always a good way to proceed, but it is informative here because of the large number of tests and the
underlying concern that an apparent relationship for one period will not persist in later data.
14. The excess returns on fixed-income securities are termed “bonds” for brevity in the table and the text.
15. Six months is, of course, a short horizon in other contexts.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 4
Economic Activity and Stock and Bond Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

P-values for Excluding Market and Industry Returns from Basic Equation
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.13
0.16
0.57
0.87
0.18
0.04

0.00
0.00
0.08
0.38
0.42
0.37

0.00
0.00
0.05
0.05
0.50
0.23

0.13
0.19
0.10
0.60
0.87
0.78

Consumption
Total
Durable goods
Nondurable goods and services

0.40
0.34
0.37

0.08
0.10
0.08

0.24
0.05
0.53

0.55
0.47
0.46

Labor market
Unemployment rate
Real labor income

0.01
0.12

0.00
0.68

0.00
0.19

0.00
0.04

Inflation rate

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.15
0.00
0.49

0.13
0.00
0.48

0.02
0.00
0.09

0.00
0.00
0.00

P-values for Excluding Bond Returns from Basic Equation
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.04
0.06
0.43
0.19
0.12
0.46

0.01
0.02
0.23
0.30
0.99
0.71

0.23
0.24
0.16
0.26
0.91
0.85

0.84
0.63
0.19
0.21
0.26
0.75

Consumption
Total
Durable goods
Nondurable goods and services

0.13
0.31
0.14

0.03
0.03
0.20

0.04
0.02
0.29

0.05
0.02
0.07

Labor market
Unemployment rate
Real labor income

0.40
0.68

0.06
0.70

0.20
0.82

0.62
0.75

Inflation rate

0.07

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.01
0.00
0.00

0.06
0.08
0.00

0.00
0.54
0.00

0.07
0.15
0.00

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

At the one-month and three-month horizons, news
in bond returns generally is related to industrial
production—total and manufacturing. At horizons
beyond one month, bond returns are related to total
consumption and consumption of durable goods in
particular. At all horizons beyond one month, bond
returns also are related to inflation. Excess bond
returns appear to be more closely related to excess
stock returns and the Treasury bill rate than to the
excess bond return although there is some relationship between news in excess bond returns and
excess bond returns in the next few months.
Is the news in stock returns due to news reflected in
the aggregate market return, or is there substantial
information in returns by industry? Table 5 presents
evidence on this issue. The first two parts of the table
show p-values for excluding aggregate stock returns
and for excluding industry returns from the basic equation with both aggregate and industry returns included
in the regressions. The second two parts of the table
show p-values for excluding market stock returns with
industry returns excluded and for excluding industry
returns with the market return excluded.
Even though Table 4 shows that news in the combination of aggregate stock market and industry
returns is important, a pattern is evident in the first
two parts of Table 5—news in neither the market
return nor the industry returns seems to be generally
important. A glaring exception is the informativeness of the industry returns for inflation—a surprising result.16 These p-values, though, are for tests
that drop the market return with industry returns
included in the regression and for tests that drop the
industry returns with the market return included.
Correlation of the aggregate market return and
the industry returns is a plausible explanation of
these results. It may not matter whether the market
return or the set of industry returns is included as
long as one of the two is included. In fact, it would
not be entirely surprising if the regressions might
include either the aggregate return or the set of
industry returns because the aggregate market
return is a weighted average of the industry returns
with time-varying weights. This relationship between
the market return and the industry returns suggests
that correlation of the aggregate return and the
industry returns may well explain why either can be
deleted with the other left in the regressions.17

The importance of the correlation of the industry
and market returns is supported by comparing the
p-values in the last two parts of Table 5 with the pvalues in the first two parts. Both the market return
and the set of industry returns have low p-values if
the other is excluded. Overall, the p-values in Table 5
indicate that it is important to include either the
market return or the industry returns, but once one
is included, the other generally is uninformative.
Is there some reason to prefer the market return
or the industry returns? There is little evidence in
Table 5 to support a choice of one over the other.18
The market return appears to be more closely related
to industrial production, especially at a horizon of one

The stock market does not necessarily decline
substantially before a recession, but the onset
of a recession is invariably associated with a
substantial decline in stock prices.

year. The industry returns appear to be more closely
related to inflation. On the other hand, the market
return has one estimated coefficient instead of the
eight coefficients for industry returns. Fitting well in
sample and predicting poorly out of sample is likely to
be less of a problem with one estimated coefficient
than with eight. In the rest of the article, we report
results based on estimates with the market return but
not the industry returns included in regressions.
Appendix tables show statistics for evaluating the
informativeness of forecasting with both the market
return and industry returns as well as with industry
returns alone. Forecasts with the industry returns
and market return are roughly as accurate as forecasts with the market return or industry returns.
The economic significance of news in excess
returns. While p-values are measures of statistical
significance, they do not provide a measure of the
economic significance of the news in returns for
unexpected economic activity. This section reports
statistics that summarize the economic significance
of the news.

16. The inflation rate reflects price changes in the entire economy, and it is not obvious why financial news by industry should
be informative about this aggregate variable.
17. Even though the market portfolio is an aggregate of the industry portfolios, the industry returns can be less informative than
the market return in a linear regression because the market return is not a constant linear function of the industry returns.
18. This result is in contrast with Lamont’s (2001) for a different period.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 5
Stocks versus Industry Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

P-values for Excluding Market Return from Basic Equation When Industry Returns Included
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.57
0.63
0.68
0.78
0.49
0.78

0.87
0.98
0.67
0.47
0.93
0.40

0.77
0.72
0.82
0.15
0.30
0.23

0.79
0.74
0.81
0.46
0.74
0.88

Consumption
Total
Durable goods
Nondurable goods and services

0.64
0.68
0.91

0.45
0.95
0.12

0.95
0.67
0.62

0.74
0.70
0.37

Labor market
Unemployment rate
Real labor income

0.29
0.09

0.98
0.76

0.65
0.50

0.64
0.56

Inflation rate

0.29

0.14

0.93

0.32

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.93
0.02
0.74

0.22
0.04
0.48

0.29
0.07
0.05

0.25
0.04
0.37

P-values for Excluding Industry Returns from Basic Equation When Market Return Included
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.31
0.27
0.58
0.87
0.20
0.06

0.31
0.29
0.20
0.29
0.60
0.71

0.79
0.75
0.10
0.44
0.79
0.76

0.87
0.88
0.44
0.52
0.97
0.95

Consumption
Total
Durable goods
Nondurable goods and services

0.67
0.71
0.36

0.11
0.08
0.15

0.36
0.26
0.43

0.48
0.41
0.37

Labor market
Unemployment rate
Real labor income

0.04
0.08

0.14
0.69

0.48
0.63

0.44
0.30

Inflation rate

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.11
0.00
0.50

0.43
0.03
0.70

0.13
0.03
0.28

0.28
0.00
0.50

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

T A B L E 5 (continued)

Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

P-values for Excluding Market Return from Basic Equation When Industry Returns Not Included
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.04
0.08
0.30
0.41
0.22
0.11

0.00
0.00
0.03
0.00
0.10
0.04

0.00
0.00
0.07
0.00
0.06
0.00

0.00
0.00
0.01
0.65
0.13
0.09

Consumption
Total
Durable goods
Nondurable goods and services

0.05
0.03
0.32

0.13
0.56
0.07

0.10
0.01
0.88

0.62
0.51
0.86

Labor market
Unemployment rate
Real labor income

0.02
0.99

0.00
0.09

0.00
0.01

0.00
0.00

Inflation rate

0.12

0.01

0.03

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.83
0.00
0.28

0.02
0.00
0.08

0.00
0.00
0.00

P-values for Excluding Industry Returns from Basic Equation When Market Return Not Included
Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

0.10
0.12
0.48
0.81
0.15
0.02

0.00
0.00
0.05
0.02
0.33
0.34

0.00
0.00
0.04
0.06
0.52
0.24

0.09
0.14
0.06
0.55
0.82
0.69

Consumption
Total
Durable goods
Nondurable goods and services

0.32
0.26
0.28

0.06
0.07
0.12

0.17
0.03
0.45

0.47
0.38
0.44

Labor market
Unemployment rate
Real labor income

0.01
0.19

0.00
0.40

0.00
0.15

0.00
0.03

Inflation rate

0.00

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

0.10
0.00
0.40

0.14
0.00
0.42

0.01
0.00
0.00

0.04
0.00
0.00

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 6
The Percentage of Variation in Unexpected
Economic Activity Predicted by News in Financial Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

Industrial production
Total
Manufacturing
Mining
Consumer durable goods
Consumer nondurable goods
Utilities

2.60
2.20
0.80
1.30
1.20
1.00

4.60
4.10
1.60
2.10
0.50
0.90

4.00
3.90
1.70
2.50
0.90
1.40

2.00
2.10
2.00
1.40
1.30
0.70

Consumption
Total
Durable goods
Nondurable goods and services

2.30
2.10
1.40

4.00
4.30
1.90

4.40
6.70
1.40

2.80
4.20
2.00

Labor market
Unemployment rate
Real labor income

1.60
0.50

3.50
0.70

4.00
1.40

3.20
2.00

Inflation rate

2.70

5.40

4.50

4.40

2.20
5.40
19.80

2.40
4.40
27.30

4.20
3.20
20.40

2.70
3.00
19.20

Financial market returns
Excess stock return
Excess bond return
Treasury bill rate

Analysis. Table 6 presents estimates of the closeness of the relationship between unexpected economic activity and unexpected excess returns,
measured by the percentage of the variation of
otherwise unexpected economic activity associated
with unexpected excess returns.19 The estimates are
noisy because unexpected economic activity and
unexpected returns are not directly observable and
must be estimated, and the estimates of expected
returns and expected economic activity are themselves noisy. As a result, these estimates are likely to
understate the value of the news in unexpected
returns, especially if part of what we estimate to be
unexpected activity really was expected and unexpected returns are well estimated.20 Even so, the
estimates in Table 6 are not exactly overwhelming.
For example, the highest percentage of variation for
a variable other than a financial asset return is 5.4
percent for inflation at the three-month horizon.
This percentage is not high enough to inspire confidence in using the estimated relationship for hedging. The percentages of variation predicted by news
in excess returns are positive, but they are far from
the maximum value of 100. These estimates provide
a partial measure of unexpected news’ importance:
News in financial returns has some information
14

about future unexpected economic activity, but it is
far from perfect. An alternative estimate of the relative importance of news is its importance for forecasting, which is examined next.
Forecasting using excess returns. Tables 3
through 5 indicate that financial assets’ excess
returns do contain news about future economic
activity. While informative, this evidence is not really
sufficient to ensure that the excess returns are useful for forecasting because the estimated regressions
are based on the data that the excess returns supposedly are forecasting. How well do these returns
help to predict the future when the relationship is
estimated based only on the past?
Tables 7 and 8 summarize the results of using
rolling regressions estimated using data for successive twenty-year periods to evaluate the out-ofsample performance of the financial returns. For
each month, we estimate the basic equation (12)
using data for the most recent twenty years and
make a forecast for a horizon from one to twelve
months. The forecasts are “out of sample” because
the forecast is made for a period that is not included
in the estimated regression. Running these regressions for every possible period generates a set of
forecasts for every possible month.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

Not all the measures of economic activity included
in Tables 3 through 6 are included in Tables 7 and 8.
The evidence in Tables 3 through 6 suggests financial
news generally is not informative for some of the measures of economic activity in these tables, and there is
little reason to buttress that evidence by showing that
financial news does not help to predict these measures.21 Tables 7 and 8 include industrial production—total, manufacturing, and consumer durable
goods—consumption of durable goods, the unemployment rate, the inflation rate, the excess returns
on stocks and bonds, and the Treasury bill return.
Table 7 summarizes the forecasting ability of the
regressions at various horizons by their meansquared errors (MSEs) and R2s. The table presents
the MSEs of forecasts based on the rolling regressions
and, for comparison, the MSEs of forecasts based on
the estimated regressions for the whole period. The
estimated regressions for the whole period can be considered in-sample regressions that can be contrasted
with the rolling regressions used to forecast out of
sample. The rolling regressions’ MSEs in Table 7 are
higher than the in-sample MSEs, a result that is not
surprising. The regressions for the whole period are
the minimum MSEs from constant regressions for the
period; the rolling regressions have extra flexibility
because the estimated coefficients can change over
time, but the rolling regressions cannot fit idiosyncratic changes in the data and then forecast those
same idiosyncratic changes. Apparently, too good a fit
is a more serious issue than changing coefficients.
Table 7 also shows out-of-sample R2-like measures
(1 – rolling regression MSE/variance of the measure
of economic activity) for the rolling regressions and,
for comparison, the R2s for the regressions estimated
for the whole period. The out-of-sample R2s are lower.
The lower fit out of sample indicates that deterioration of forecast accuracy compared to in-sample fit
has to be considered when using tracking portfolios
for forecasting or for hedging risks.
The rolling regressions have the highest R2s for
the Treasury bill return, the inflation rate, and the
unemployment rate. The R2s for the Treasury bill
return are quite high, but enthusiasm over the
rolling regressions’ R2s—from 82 percent one month

ahead to 65 percent twelve months ahead—must be
tempered by the likelihood that the prior month’s
Treasury bill return included in the rolling regressions plays a large role in these relatively high R2s.
The R2s for the inflation rate are not as high, ranging
from 44 percent at the nine-month horizon to 16
percent at the twelve-month horizon. But enthusiasm over these results must be tempered somewhat
again by the realization that the prior year’s inflation
rate is included in the rolling regressions. While not
particularly helpful for explaining the unemployment rate a month ahead, the forecasts from the
rolling regressions predict 27 percent of changes in
the unemployment rate over the next year, with no

Expected returns on stocks and bonds are
affected by developments in the economy, and it
is impossible for those developments to affect
future expected returns without affecting prices
and current returns.

lagged unemployment rate in the regressions; this
result is the most promising one in the table.
The rolling regressions forecast total and manufacturing industrial production much worse than the
in-sample fit suggests, but the R2 of 20 percent for
industrial production over the next six months is not
entirely trivial.22 The rolling regressions uniformly
have negative R2s for the excess returns on stocks
and bonds, which indicates that a forecast of a recent
average might have been better than these forecasts
conditional on past financial returns and economic
activity. Interestingly, given its cyclical sensitivity, the
rolling regressions also are particularly poor at forecasting growth of consumption of durable goods.
The rolling regressions are estimated regressions
for each month based on data for the most recent
twenty years; the forecasts over the horizons can be
broken into two parts—one part due to the excess
returns in the economic tracking portfolio and the

19. In other words, Table 6 shows the R2s for the estimates of equation (11) times 100.
20. Variables that are left out and uncorrelated with the excess returns raise the residual variance of the regression including
the financial returns. Because they are uncorrelated with the excess return, the variables left out do not affect the estimated
increase in the residual variance associated with deleting the financial returns. Hence, the marginal R2, which is the change
in the residual variance divided by the residual variance with all variables, is lower than it would be otherwise. Table 6
reports this marginal R2 times 100.
21. The statistics in Tables 7 and 8 for measures of economic activity not included in the table show that financial returns are
not useful for forecasting variables that are unrelated to financial returns.
22. Recall, though, that the prior year’s growth of total industrial production is included in the regressions.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 7
Forecast Accuracy
Stock Market Returns and Bond Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

Industrial production total
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.54
0.42
0.11
0.31

2.57
1.59
0.18
0.49

7.45
3.63
0.15
0.58

21.67
7.25
0.04
0.68

Industrial production manufacturing
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.67
0.52
0.11
0.30

3.11
1.94
0.19
0.49

8.69
4.27
0.20
0.61

25.73
8.96
0.08
0.68

Industrial production of consumer
durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

6.30
5.33
–0.03
0.13

21.77
14.88
–0.02
0.30

40.60
23.65
0.07
0.46

76.08
34.71
0.10
0.59

Consumption of durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

10.14
8.65
–0.07
0.09

17.14
12.83
–0.09
0.18

24.89
15.57
–0.07
0.33

53.71
25.24
–0.43
0.33

Unemployment rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.03
0.02
0.08
0.23

0.10
0.07
0.25
0.50

0.31
0.15
0.24
0.62

0.87
0.34
0.27
0.71

Inflation rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.07
0.06
0.35
0.47

0.39
0.24
0.43
0.65

1.27
0.61
0.44
0.73

6.86
1.76
0.16
0.78

Excess stock return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

22.71
19.76
–0.07
0.08

73.83
59.06
–0.10
0.12

161.22
102.97
–0.22
0.22

351.71
183.77
–0.32
0.31

Excess bond return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

8.83
7.54
–0.03
0.12

29.49
23.27
–0.05
0.17

57.45
40.16
–0.08
0.24

148.35
65.46
–0.26
0.44

Treasury bill return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.01
0.01
0.82
0.86

0.07
0.05
0.82
0.88

0.38
0.22
0.75
0.86

2.01
0.79
0.65
0.86

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TABLE 8
Contribution of Financial News to the Forecasts
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

Industrial production total
Constant
Financial news
Other variables

0.037
0.090
0.653

0.168
0.332
0.628

0.285
0.367
0.605

0.826
0.644
0.526

Industrial production manufacturing
Constant
Financial news
Other variables

0.038
0.064
0.665

0.193
0.331
0.633

0.294
0.430
0.627

0.863
0.672
0.546

Industrial production of consumer
durable goods
Constant
Financial news
Other variables

0.028
–0.061
0.527

0.227
0.024
0.519

0.160
0.567
0.556

0.227
0.898
0.562

Consumption of durable goods
Constant
Financial news
Other variables

0.520
0.456
–0.141

0.896
0.830
0.254

1.379
0.730
0.394

3.762
0.617
0.331

Unemployment rate
Constant
Financial news
Other variables

0.008
–0.094
0.660

0.020
0.164
0.695

0.031
0.434
0.650

0.048
0.891
0.650

Inflation rate
Constant
Financial news
Other variables

0.082
0.345
0.802

0.353
0.815
0.734

0.776
0.499
0.712

2.278
0.762
0.564

Excess stock return
Constant
Financial news
Other variables

0.282
0.218
0.289

0.923
0.349
0.280

1.952
0.477
0.207

3.828
0.152
0.177

Excess bond return
Constant
Financial news
Other variables

0.094
0.543
0.301

0.283
0.522
0.333

0.524
0.497
0.400

0.620
0.401
0.310

Treasury bill return
Constant
Financial news
Other variables

0.000
0.923
1.068

0.031
1.281
1.054

0.095
1.289
1.060

0.430
1.469
1.017

Note: For each variable and horizon, the three numbers listed are the estimated constant term, estimated coefficient for the improvement
in the forecast due to including the estimated news in financial returns, and the estimated coefficient for the forecast using the variables
other than the unpredictable part of financial returns.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

other part due to other variables that help predict
expected economic activity. Table 8 summarizes the
value of adding financial news to forecasts by comparing forecasts based on the basic equation (12) to
forecasts excluding the estimated news in financial
returns. Table 7 is based on rolling-regression estimates of the basic equation (12), and the forecast
values from these rolling regressions can be denoted
f r. z.23 The superscripts r and z reflect the financial
returns, r, and the other variables, z, included in the
regressions. Rolling regressions also can be estimated without the recent returns on financial assets,
and these regressions can be used to generate forecast values that can be denoted f z.
What is the additional value of using the returns
to make forecasts? A standard way of combining
forecasts is to regress the actual values of the series
on the two forecasts of the series, with the two coefficients reflecting the relative value of the two forecasts.24 In our application, the two forecasts are
correlated because they are based on the common
base set of variables, z; the coefficient on the forecast including financial returns, f r.z, is the value of
that forecast, not the marginal improvement in forecasts by adding financial returns. To estimate the
more informative improvement in forecasts by
adding financial market returns, we estimate the
parts of the forecast f r.z from rolling regressions
that are uncorrelated with f z and include them in
the forecast regressions with f z.25
Table 8 presents the results of estimating these
regressions for horizons of one to twelve months.26 If
the forecasts were unbiased, the constant terms
would be zero and the sums of the two coefficients
would be one. Because the forecasts underlying
Table 8 are based on rolling regressions, the constant terms in Table 8 need not be zero and the sums
of coefficients need not add up to one; the regressions generally do not satisfy these restrictions.
With variation by horizon, the news in financial
returns is useful for all the series. For example,
financial news is uninformative for changes in the
unemployment rate over the next month and is
more informative than other variables over the next
year. The overall picture in Table 8 is one of financial
news being informative about the future in addition
to, and often more than, the other variables.

Conclusions and Discussion
he evidence in this article shows that movements in financial markets do presage developments in the economy. In one sense, this evidence
is not surprising. Expected returns on stocks and
bonds are affected by developments in the economy, and it is impossible for those developments to
affect future expected returns without affecting
prices and current returns. In another sense, the
results are surprising. Evidence on the connection
between movements in financial markets and the
economy is mixed, with conclusions that typically
do not survive scrutiny in a succeeding paper or the
passage of time. This article provides evidence of
exactly that pattern: Lamont’s (2001) evidence that
industry returns are useful complements of the
market return is not borne out by the experience of
the 1990s.
What is to say that the results of this study will
hold up? Our conclusion is extremely general:
Returns on financial markets are informative about
future developments in the economy. We believe
this conclusion is unlikely to be affected by variations in technique or the passage of time, but only
future research and time will tell. As it stands, the
evidence indicates that news revealed in financial
markets helps to predict future economic activity.
Whether the passage of time will be kind to other
conclusions also remains to be seen. We find that
asset returns are informative about both real developments, such as industrial production, and inflation.
We find that returns on both stocks and bonds are
informative about future economic activity and that
industry returns are no more informative about future
economic activity than is the overall market return.
We also find that forecasts based on data actually
available before a forecast is made are noticeably less
informative than is suggested by computed forecasts
based on subsequent data available later.
The deterioration of forecasts with rolling
regressions compared to forecasts based on all the
data is not inevitable. The reasons for such deterioration, other than the trivial one of using fewer
observations, are likely to be informative about the
relationship between asset returns and economic
activity, which can itself inform knowledge about
asset returns and about the economy.

23. The subscript t is suppressed for notational simplicity.
24. Diebold et al. (1996) provide a convenient summary of the literature.
25. Operationally, the additional information in the basic equation due to financial market returns is estimated by the residuals
from a regression of f r. z on f z.
26. Reported standard errors are calculated using the Newey and West (1987) correction with a truncation lag of twelve months.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

APPENDIX
Data and Sources

his appendix presents details about the data
and the sources. All growth rates are continuously compounded.

Economic Activity

• The growth rate of industrial production is the
change in the logarithm of total production,
seasonally adjusted. Industrial production by
sectors is included for manufacturing production, consumer durables, consumer nondurables, mining, and utilities. These series
are seasonally adjusted and are from Data
Research Inc. (DRI).
• The growth rate of consumption is the
change in the logarithm of total real consumption. Consumption also is analyzed for
the component parts, consumption of durable
goods and consumption of nondurable goods
and services. All series are seasonally adjusted
and expressed in real terms using deflators
from DRI for the corresponding part of total
consumption.
• The growth rate of real labor income is the
change in the logarithm of personal income
from wages and salaries, seasonally adjusted,
minus the inflation rate measured by the consumer price index (CPI) for all consumers.
Personal income from wages and salaries and
the inflation rate are from DRI.
• The unemployment rate is the total unemployment rate for all workers sixteen years and over,
seasonally adjusted. This series is from DRI.
• The inflation rate is the change in the logarithm of the CPI for all urban consumers, not
seasonally adjusted. This series is from DRI.
• The excess return on the CRSP value-weighted
index is the continuously compounded return
on the CRSP value-weighted index minus the
continuously compounded return on Treasury
bills. Both series are from CRSP.
• The excess return on long-term government
bonds is the continuously compounded return
on a portfolio of long-term government bonds
minus the continuously compounded return
on Treasury bills. The return on long-term
bonds is from Ibbotson Associates, and the
return on Treasury bills is from CRSP.
• The return on Treasury bills is the continuously compounded return on a Treasury bill
from CRSP.

Returns

• The aggregate stock price index is the CRSP
NYSE-AMEX-Nasdaq value-weighted stock
market portfolio.
• Industry portfolios are computed in two ways.
The returns in the paper are based on industries calculated as in Lamont (2001) and Sharpe
(1982). For each year, the industry indexes
are based on every NYSE-AMEX-Nasdaq stock
being assigned to an industry portfolio based
on its four-digit standard industrial classification (SIC) code at the end of June. This classification then is used for returns computed until
the following June. Returns are then computed
from the end of the month to the end of the
next month. The industry definitions are from
Sharpe (1982). The eight industry stock portfolios (and their SIC codes) are finance (6000–
6999), utilities (4800–4829, 4900–4999), transportation (3720–3799, 4000–4799), energy
(1300–1399, 2900–2999), basic industries
(1000–1299, 1400–1499, 2600–2699, 2800–
2829, 2879–2899, 3300–3399), capital goods
(3400–3419, 3440–3599, 3670–3699, 3800–
3849, 5080–5089, 5100–5129, 7300–7399),
construction (1500–1999, 2400–2499, 3220–
3299, 3430–3439, 5160–5219), and consumer
goods (0000–0999, 2000–2399, 2500–2599,
2700–2799, 2830–2869, 3000–3219, 3420–3429,
3600–3669, 3700–3719, 3850–3879, 3880–3999,
4830–4899, 5000–5079, 5090–5099, 5130–5159,
5220–5999, 7000–7299, 7400–9999).
French’s classification of industries produces
similar results. For each year, the industry
indexes are based on every NYSE-AMEXNasdaq stock being assigned to an industry
portfolio based on its four-digit SIC code at the
end of June. This classification then is used for
returns computed until the following June.
Returns are then computed from the end of
the month to the end of the next month. The
five industry stock portfolios (and their SIC
codes) are manufacturing (2000–3999), utilities
(4900–4999), shops (wholesale, retail, and some
services (5000–5999, 7000–7999), finance
(6000–6999), and other. The five industry stock
portfolios were downloaded from Kenneth
French’s Web page <mba.tuck.dartmouth.edu/
pages/faculty/ken.french/data_library.html>
(June 12, 2003).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

A P P E N D I X (continued)
TABLE A
Forecast Accuracy
Stock Market, Industry, and Bond Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

Industrial production total
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.58
0.42
0.04
0.32

2.71
1.56
0.13
0.50

7.96
3.54
0.09
0.59

23.72
7.14
–0.06
0.68

Industrial production manufacturing
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.70
0.51
0.07
0.31

3.23
1.89
0.16
0.51

9.24
4.14
0.15
0.62

28.22
8.83
–0.01
0.68

Industrial production of consumer
durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

6.75
5.24
–0.10
0.15

23.07
14.45
–0.08
0.32

43.26
22.83
0.01
0.48

84.97
33.63
–0.00
0.60

Consumption of durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

10.72
8.51
–0.13
0.10

17.49
12.32
–0.11
0.21

25.47
14.64
–0.09
0.37

57.79
24.48
–0.54
0.35

Unemployment rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.03
0.02
0.03
0.25

0.11
0.07
0.22
0.52

0.32
0.15
0.21
0.63

0.93
0.33
0.22
0.72

Inflation rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.07
0.05
0.35
0.52

0.38
0.22
0.44
0.68

1.26
0.55
0.45
0.76

7.02
1.59
0.14
0.80

Excess stock return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

23.66
19.18
–0.10
0.10

76.92
57.59
–0.14
0.14

167.20
99.19
–0.27
0.25

367.55
179.33
–0.38
0.35

Excess bond return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

8.89
6.96
–0.04
0.19

30.86
22.60
–0.10
0.19

60.62
38.77
–0.14
0.27

149.11
62.27
–0.26
0.47

Treasury bill return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.01
0.01
0.82
0.87

0.07
0.05
0.82
0.88

0.39
0.21
0.75
0.86

2.11
0.78
0.64
0.86

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

12 Months

• Four bond returns are also included in the
regressions. The bond returns are for a longterm government bond, an intermediate-term
government bond, a one-year government bond,
and a high-grade corporate bond. The long-term,
intermediate-term, and high-grade bond returns
are from Ibbotson Associates (see the Stocks,
Bonds, Bills, and Inflation 2003 Yearbook for
further details). The one-year bond return is
from CRSP. All rates of return in the regressions
are excess returns relative to the one-month
Treasury bill return from CRSP and are based on
month-end bid-ask average values.
Additional Variables

Estimates of the expected value of economic
activity and excess returns are based on a constant term and lagged values of variables. Eight
lagged variables are included that have been used
in tests of multiple-beta models and studies of
stock-bond return predictability in, among others,
Chen, Roll, and Ross (1986); Burmeister and
McElroy (1988); Ferson and Harvey (1991, 1999);
Downs and Snow (1994); Kirby (1998); Balduzzi
and Robotti (2001); and Lamont (2001). Some of
these lagged variables span the prior twelve
months and some span only the prior month.
Variables for the prior twelve months
• The inflation rate for the prior twelve months is
the change over the prior twelve months in the
logarithm of the CPI, not seasonally adjusted,
from DRI.
• The excess aggregate stock return is the aggregate return (including dividends) on the NYSEAMEX-Nasdaq value-weighted stock market

portfolio from CRSP for the prior twelve months
minus the one-month Treasury bill return over
the past twelve months from CRSP.
• The growth rate of industrial production for the
prior twelve months is the change in the logarithm of total production, seasonally adjusted,
from DRI.
Variables for the prior month
• The prior month’s term premium on one-year
Treasury securities is the yield on the one-year
constant maturity note from Global Financial
Data Inc. minus the thirty-day Treasury bill
yield from CRSP.
• The prior month’s long-term premium is the
yield on long-term government bonds minus
the one-month Treasury bill yield. The yield
on long-term government bonds is from
Ibbotson Associates, and the Treasury bill
yield is from CRSP.
• The return on a one-month Treasury bill is
from CRSP.
• The default premium on short-term debt is the
yield on commercial paper minus the one-month
Treasury bill yield. The commercial paper rate is
from various issues of Banking and Monetary
Statistics, Annual Statistical Digest, and
Domestic Financial Statistics. The one-month
Treasury bill rate is from CRSP.
• The default premium on corporate securities
is the BAA yield on corporate debt minus the
AAA yield on corporate debt. Both series are
from DRI.
• The prior month’s dividend yield is the annualized dividend yield on the S&P 500 composite
common stock. The series is from DRI.

TABLE B
Forecast Accuracy
Industry and Bond Returns
Horizon
Measure of economic activity

1 Month

3 Months

6 Months

12 Months

Industrial production total
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.57
0.42
0.06
0.31

2.66
1.57
0.15
0.50

7.85
3.55
0.10
0.59

23.35
7.15
–0.04
0.68

Industrial production manufacturing
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.69
0.52
0.08
0.31

3.19
1.90
0.17
0.50

9.10
4.16
0.16
0.62

27.72
8.83
0.01
0.68
(continued)

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

A P P E N D I X (continued)
T A B L E B (continued)
Horizon
Measure of economic activity

1 Month

3 Months

Industrial production of consumer
durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

6.68
5.24
–0.09
0.15

23.03
14.51
–0.08
0.32

43.07
23.06
0.02
0.47

82.21
33.63
0.03
0.60

Consumption of durable goods
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

10.57
8.51
–0.12
0.10

17.35
12.32
–0.10
0.21

25.19
14.65
–0.08
0.37

57.28
24.50
–0.53
0.35

Unemployment rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.03
0.02
0.05
0.25

0.10
0.07
0.24
0.52

0.32
0.15
0.21
0.63

0.92
0.33
0.22
0.72

Inflation rate
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.07
0.05
0.36
0.52

0.38
0.22
0.44
0.68

1.25
0.56
0.45
0.76

6.89
1.59
0.16
0.80

Excess stock return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

23.40
19.18
–0.09
0.10

76.71
57.97
–0.14
0.14

166.98
99.22
–0.27
0.25

364.82
179.38
–0.37
0.32

Excess bond return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

8.81
6.99
–0.03
0.18

30.79
22.69
–0.10
0.19

60.34
38.90
–0.13
0.27

150.11
62.83
–0.27
0.47

Treasury bill return
Rolling MSE
In-sample MSE
Rolling R2
In-sample R2

0.01
0.01
0.82
0.87

0.07
0.05
0.82
0.88

0.39
0.22
0.75
0.86

2.08
0.78
0.64
0.86

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

6 Months

12 Months

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Flannery, Mark J., and Aris A. Protopapadakis. 2002.
Macroeconomic factors do influence aggregate stock
returns. Review of Financial Studies 15, no. 3:751–82.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

Leading Indicators of Country
Risk and Currency Crises:
The Asian Experience
MARCELLE CHAUVET AND FANG DONG
Chauvet is a research economist at the Atlanta Fed. Dong is an
assistant professor at Providence College in Rhode Island.

n recent years, capital restrictions in emerging
markets have been substantially reduced. As a
result, international financial flows to these
countries have risen. Most emerging markets
have adopted a pegged exchange rate system
in which central banks are committed to keeping their domestic currency in terms of the U.S. dollar
within narrow bands. Under this system, a country can
finance a current account deficit from its reserves
or by borrowing from abroad. That is, the country
can buy time in handling external deficits without
decreasing the monetary base or reducing the public
deficit. Such a regime relies on a delicate balance and
makes a country vulnerable to shocks in mobile international capital markets, especially with respect to
outflows in bank deposits.
When international markets are relatively calm,
lenders may be willing to finance countries with
mildly weak fundamentals. As international conditions
deteriorate, however, investors’ perception about a
borrower’s creditworthiness may change. Economies
that look sound one moment seem riskier the next—
not necessarily because of new developments within
their borders but perhaps because interconnected
countries are in distress. As foreign investors become
more risk averse, they may withdraw short-term
investments and sell local currency. The country’s
central bank must then increase interest rates sufficiently to dampen the outflow and avoid a collapse
of the pegged exchange rate system. The result of

such reactive strategies may be a credit crunch that
spreads from country to country, driving each into
economic recessions with high inflation.
In the last decade several developed and developing countries experienced currency crises. For
example, the European Monetary System (EMS) was
severely undermined by intense speculative pressure in 1992–93, which led to the exit of Britain and
Italy in 1992. More recently, several emerging market
economies underwent large devaluations of their
currencies: Mexico in 1994, several Asian countries
in 1997, Russia in 1998, and, subsequently, Brazil in
1999, among others. These events cast a bleak outlook for the global financial system and caused
widespread economic distress. Even the U.S. economy experienced slowdowns associated with these
international events, especially the Mexican and
Asian crises.
A “country risk” of currency crisis is not directly
observable, but prior currency pressures can be
detected in several sectors of the economy. In particular, financial variables reflecting investors’ expectations and banking distress are highly sensitive to
changes in the economic environment. This article
aims to construct an early warning system for international currency crises using such variables. The
system uses a dynamic factor model with regime
switching to construct leading indicators of country
risk and currency crises. In this model, an unobservable factor switches regimes, representing periods

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

of relative calmness and periods prone to currency
crises, using a two-state Markov process. The method
is applied to evaluate the model’s in-sample and
out-of-sample performance in anticipating currency
crises in the last two decades in Thailand, Indonesia,
and Korea. The dynamic factor index gives early distress signals of country risk and currency crisis, using
several financial and banking variables.
Leading indicators have been a successful forecasting tool adopted by the National Bureau of
Economic Research (NBER) since the work of Burns
and Mitchell (1946). New econometric models have
now been used to explore more formally potential
dynamic differences across cycle phases in several

This article constructs an early warning system
for international currency crises using financial
variables that reflect investors’ expectations
and banking distress.

variables. The method used to construct economic
indicators is distinct from econometric regression
methods. In particular, the goal is not to form a forecast of exchange rates based on the information set.
Instead, leading indicators are indexes composed of
several variables, designed to give early signals of
major cyclical changes in exchange rates, particularly the beginning and end of cyclical phases (that
is, their turning points). Variables that exhibit low
power in explaining the linear long-run variance of
exchange rates may be highly important in specific
situations. In fact, unusually large changes in some
variables at particular historical episodes—as
opposed to the linear average behavior of the series—
can be important independent factors in determining large exchange rate devaluations.
A large theoretical and empirical literature aims
to characterize or forecast the recent experiences
of currency crises.1 Few of these studies, however,
focus on forecasting turning points representing
episodes of speculative attacks.
The method this study uses to construct indicators
differs from the previous currency crisis literature in
several ways. First, since currency crises are caused
by different shocks over time, the inclusion of different variables increases the model’s ability to signal
future crises. In addition, the combination of variables
reduces measurement errors in the individual series
26

and smooths out noise inherent in monthly data. This
smoothing reduces the likelihood of signaling false
turning points, which can be a significant problem in
the monthly frequency. Second, in contrast to composite indicators that are constructed as weighted
averages of statistical transformations of their components, the dynamic factor model takes into account
cross-correlations and potential long-term relationships among the variables. Finally, the method yields
probabilities that can signal turning points in real
time. This method contrasts with the rules of thumb
used to build some composite indicators, which
require the use of substantial ex post data. Because
these rules are based on the unusual behavior of some
variables compared to their frequency distribution,
turning points can be identified and predicted only
a couple of months after their occurrence, which
undermines their usefulness for real-time forecasting.
Thus, the advantage of the proposed approach in
comparison with alternative models and rules of
thumbs is that it treats foreign exchange market
regimes as unobservable priors instead of observed
ex post events, and no ad hoc criterion is adopted in
determining the crisis state. Instead, the model generates regime probabilities from the leading indicators that can be used to signal increases in country
risk and potential currency crises in real time.
The approach in this article implements several
linear and nonlinear methods to select the variables
composing the indicators. For the Asian countries
studied, the best candidates are monetary and
banking series. The study shows that the leading
indicators built from the nonlinear dynamic factor
model unveil, both in sample and out of sample,
early warning signals of an increase in the country
risk and subsequent depreciation of nominal
exchange rates experienced by Thailand, Indonesia,
and Korea, especially before the 1997 crisis. In general, phases of the leading index exhibiting a higher
mean and volatility precede currency crises, whereas
the noncrisis state is associated with a lower mean
and volatility.
For all the countries studied, the regime probabilities give early signals of the 1997 crisis and reveal
a contagion pattern. For Thailand, a crisis was signaled six months earlier than the actual one. For
Indonesia, the probabilities indicated a crisis seven
months before the actual one, which was minimized
by preemptive government actions. However, once
Thailand’s currency crisis hit, the probability of a crisis in Indonesia also increased substantially and thus
increased the probability of a crisis in Korea. This
finding suggests a contagion pattern that is being
further examined in ongoing projects.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

The article first discusses the currency crises
experienced by the Asian countries studied. The
discussion then presents the data and statistical
analysis used to select the leading variables, presents the dynamic factor model used to construct
the leading indicators, and reports the in-sample
and out-of-sample empirical results.

Currency Crises in Asia
adelet and Sachs (1998) study the broad features
underlying the recent experiences of currency
crises in Asian countries. One striking finding is that
typical international and domestic problems were not
present before the onset of the crises. In fact, for the
most part conditions in international financial markets, commodity markets, and the trading system
were favorable. These countries were not pursuing tight anti-inflationary policies, and their real
exchange rates were only mildly overvalued because
of the persistent inflow of capital. In addition, their
overall debt-carrying capacities did not seem to present imminent risks of default. In particular, Radelet
and Sachs find that instability in international lending
and self-fulfilling speculative attacks are the most
likely explanations for the Asian crisis in 1997.
International loan markets may be subject to selffulfilling crises even when individual creditors act
rationally. Changes in investors’ risk perception may
result in sharp, costly, and fundamentally unnecessary panicked reversals in capital flows. In this situation, exchange rates may immediately depreciate
under intense pressure. The unwillingness or inability of the capital market to provide new loans to the
illiquid borrowers is a chief factor during crises.
Another common feature of these countries prior
to the crises was the growing weaknesses in East
Asian financial systems resulting from incomplete
markets and some market-oriented reforms, which
made the countries vulnerable to capital flight. In this
regard, the intensity and propagation of the crises
were also the result of partial banking and financial
reforms that exposed these economies more directly
to the instability of international financial markets.
Examples of bank weaknesses were the growth
of short-term foreign debt, the rapid expansion of
bank credit/lending, the inadequate regulation and
supervision of financial institutions, and the sharp

increase in the number of financial institutions and
private banks (including foreign and joint venture
banks) that could borrow or lend in foreign currencies, both on- and offshore.2
These problems made the countries more vulnerable to a rapid reversal of capital flows that put
downward pressure on their currencies. Whereas
Radelet and Sachs (1998) find that the problems
were centered in the private sector rather than in
the government, this article finds that they were
also present in the monetary system.
Thailand. Three major currency devaluations in
Thailand occurred during the 1981:05–1981:07,
1984:11–1985:03, and 1997:07–1998:01 periods.3

This study demonstrates that the leading indicators of currency crises can be informative
tools for signaling future currency crises in
real time and could thus allow preemptive
counterpolicy measures by the central bank.

These devaluations of the baht are illustrated in
Figure 1, which plots Thailand’s nominal exchange
rate in the form of logarithmic first differences
(GW_N$BAHT).
During the 1990s, capital inflows into Thailand
averaged over 10 percent of gross domestic product
(GDP) and reached a remarkable 13 percent of GDP
in 1995 alone. These inflows consisted predominantly of borrowing by banks and financial institutions. Throughout the decade the government fixed
the exchange rate within very narrow bands. In effect,
the central bank absorbed the risks of exchange rate
movements on behalf of investors and thus encouraged capital inflows, especially of short-maturity
instruments. However, increasing capital inflows put
upward pressure on the prices of nontradable goods
and services. The real effective exchange rate appreciated by more than 25 percent between 1990 and
early 1997.
Indonesia. Three major currency devaluations
in Indonesia occurred in April 1983, September to

1. See, for example, the list of more than 100 recent papers and books related to the NBER Project on Exchange Rate Crises in
Emerging Market Countries at <www.nber.org/crisis> or the reference list at <www.stern.nyu.edu/globalmacro>.
2. State-owned banks in Indonesia and Korea were regularly allowed to break many prudential regulations without penalty.
3. During the 1984:11–1985:03 period, Thailand abandoned a fixed exchange rate vis-à-vis the dollar. The central bank abolished
general credit restrictions but reimposed restrictions on bank lending rates and lowered the ceiling for loans to priority sectors
(see Bekaert and Harvey 1999).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 1
Thailand’s Nominal Exchange Rate
20
97:07–98:01

10
84:11–85:03

Percent change

81:05–81:07

–5

–10

–15

–20
1980

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database

FIGURE 2
Indonesia's Nominal Exchange Rate
40
97:08–98:12

20
83:04

86:09–86:10

Percent change

–20

–40

–60

–80
1980

1983

1986

1989

Source: Datastream, International Financial Statistics database

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

1992

1995

1998

FIGURE 3
Korea's Nominal Exchange Rate
20
97:11–98:01

Percent change

–10

–20

–30

–40
1980

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database

October 1986 (Sachs, Tornell, and Velasco 1996),
and August 1997 to December 1998. The devaluations of the rupiah are shown in Figure 2, which plots
Indonesia’s nominal exchange rate in the form of
logarithmic first differences (GW_N$RUPIAH).
Capital inflows to Indonesia in the 1990s averaged a more modest 4 percent of GDP and were
mostly in the form of borrowing by private corporations. Indonesia’s government fixed the exchange
rate subject to small and predictable changes. Here
too the government absorbed the borrowing risks
undertaken by the private sector, inducing higher
inflows of capitals. As a result, the real effective
exchange rate appreciated by more than 25 percent
between 1990 and early 1997.
Korea. The only major nominal devaluation of the
Korean won was related to the Asian crisis, which hit
the country in November 1997. Annual capital inflows
averaged over 6 percent of GDP between 1990 and
1996. The government maintained the exchange rate
with small and predictable changes and absorbed the

loan risks. The real effective exchange rate appreciated by 12 percent between 1990 and early 1997.
Figure 3 plots the logarithmic first differences of
Korea’s nominal exchange rate (GW_N$WON).

Data and Statistical Analysis

election of candidate leading variables. In the
first triage, the variables were selected according to several criteria, such as their frequency, sample size, and how quickly new releases of the series
were available. For these indicators to be useful for
real-time forecasting of currency crises, the variables
used should be available at least at the monthly frequency and be timely.4 We found approximately ten
variables for each country as potential candidates to
predict abrupt changes in nominal exchange rates.
Several econometric procedures were then used
to select and rank the potential variables. First, all
series were transformed to achieve stationarity.5 The
variables were then classified according to their
cross-correlation with nominal exchange rates and

4. For example, although some series are available monthly, their release takes place two to three months later.
5. A variable is said to be (weakly) stationary if the mean and autocovariances of the series do not depend on time. Any series
that is not stationary is said to be nonstationary. The augmented Dickey-Fuller (1979) and Phillips-Perron (1988) tests were
used to test for stationarity. In addition, Perron’s (1989) test was also used to test for nonstationarity against the alternative
of deterministic trend in the presence of sudden changes in the series.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 4
Thailand: Domestic Credit, Net Foreign Assets, Private Bank Credits, and the Consumer Price Index
Domestic credit

Net foreign assets

150

100
Ab s o lu t e c h a n g e

Pe r c e n t c h a n g e

97:07–98:01

4
2
0
81:05–81:07

–2
84:11–85:03

50
0
81:05–81:07

–50
84:11–85:03

–100

–4
97:07–98:01

–6
1980

1983

1986

1989

1992

1995

1998

–150
1980

1983

Private bank credits

1989

1992

1998

1995

Consumer price index
3

97:07–98:01

97:07–98:01
81:05–81:07

Ab so lu te c h a n ge

100

1986

0
81:05–81:07

–50
84:11–85:03

2
84:11–85:03

–100
–150

–1
1980

1983

1986

1989

1992

1995

1998

1980

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database

their ability to Granger-cause exchange rates.6
Granger causality tests select variables that have a
linear predictive content for exchange rates, but
not necessarily those that perform well in anticipating peaks and troughs in exchange rate changes.
Variables that are poor predictors of linear long-run
exchange rate variances may be significant in particular situations. Large changes in such variables
during specific historical episodes can be important
in predicting large exchange rate devaluations. For
this reason, we use probability methods to study
the nonlinear relationship of each series to determine whether it anticipates peaks and troughs of
exchange rate dynamics. In particular, different
specifications of two-state first-order Markov
switching models were fitted to each candidate
leading variable (see Chauvet and Dong 2002).
The following leading variables were selected
from both linear and nonlinear procedures: (1) for
Thailand, domestic credit, net foreign assets and
private bank credits from the central bank in billions of baht, and the consumer price index (CPI)
(1995 = 100) (see Figure 4); (2) for Indonesia, the
30

money supply (M1), net foreign assets and private
bank foreign liabilities in billions of rupiah, and official foreign reserves minus gold in millions of U.S.
dollars (Figure 5); and (3) for Korea, domestic
credit, net foreign assets and private bank credits
from monetary authorities in billions of won, and
the CPI (Figure 6).
These data were obtained from the International
Financial Statistics database from Datastream. The
sample available in monthly frequency covers the
1980:01–1999:06 period for Indonesia and Korea
and the 1980:02–1999:06 period for Thailand.
Nominal exchange rates are measured in U.S. dollars per unit of the national currency.

The Dynamic Factor Model with
Markov Regime Switching
his analysis uses a dynamic factor model with
Markov regime switching to construct the leading indicators of currency crises for Thailand,
Indonesia, and Korea.7 This model is a combination
of the linear Kalman filter and Hamilton’s (1989)
Markov regime switching model and has been widely

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 5
Indonesia: M1, Net Foreign Assets, Private Bank Foreign Liabilities, and Foreign Reserves
M1

Net foreign assets

80
83:04

83:04

97:08–98:12

86:09–86:10

10
5
0
–5

40
20
0
–20

–10

–40
1980

–15
1980

1983

1986

1989

1992

1995

1998

1983

1986

Private bank foreign liabilities
100
83:04

86:09–86:10

1989

1992

1995

1998

Foreign reserves
30

97:08–98:12

83:04

97:08–98:12

86:09–86:10

20
P e rc e n t c h a n ge

60
P e rc e n t c h a n ge

97:08–98:12

86:09–86:10

60
Pe r c e n t c h a n g e

Pe r c e n t c h a n g e

40
20
0
–20
–40
–60

10
0
–10
–20
–30
–40

–80
–100

–50
1980

1983

1986

1989

1992

1995

1998

1980

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database

applied to business cycle studies (see, for example,
Diebold and Rudebusch 1996; Chauvet 1998; Kim
and Nelson 1998).
In this framework, the latent factor for each
country—the leading indicator—is constructed as
the common correlation underlying the country’s
leading financial variables. The motivation for this
setup is to combine the leading variables and
extract their common characteristics, which switch
regimes representing foreign exchange market
pressures. The mean and variance of the dynamic
factor are subject to discrete regime shifts governed
by a two-state Markov process. That is, the foreign
exchange market can be either under high pressure
to devaluate (state or regime 0) or under low speculative pressure (state or regime 1), with the alternation between states controlled by the outcome of
the Markov process. Since the probabilistic infer-

ence on crises is based on shocks to several leading
variables used for each country, the model used
here can give more accurate signals of crises (fewer
false or missed signals) than univariate autoregressive models with Markov regime switching. (See
Chauvet and Dong 2002 for further discussions.)

In-Sample Results

aximum likelihood estimates. Table 1
reports the maximum likelihood estimates of
the Markov-switching dynamic factor model for
Thailand, Indonesia, and Korea. For each country,
the analysis shows that regime 0 (high speculative
pressure) is characterized by a large variance.
For Thailand, estimation shows that the net foreign asset (NFA) variable is the most sensitive to
changes in the country’s leading indicator. A oneunit increase in the factor is associated with a

6. A Granger causality test determines how much of a current time series can be explained by past values of itself and whether
adding lagged values of another series can improve the explanation.
7. A Markov process is a simple stochastic process in which the distribution of future states depends only on the present state
and not on how the present state was achieved.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 6
Korea: Domestic Credit, Net Foreign Assets, Private Bank Credits, and the Consumer Price Index
Domestic credit
97:11–98:01

Pe r c e n t c h a n g e

6
4
2
0
–2
–4
1980

1983

1986

1989

1992

1995

Absolute change (in thousands)

Net foreign assets

97:11–98:01

8
4
0
–4
–8

1998

1980

1983

Private bank credits

1986

1989

1992

1995

1998

Consumer price index

100

3.0

2.5
Ab so lu te c h a n ge

P e rc e n t c h a n ge

97:11–98:01

60
40
20
0

2.0
1.5
1.0
0.5
0.0

–0.5

–20

97:11–98:01

1980

1983

1986

1989

1992

1995

–1.0

1998

1980

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database

monthly decrease in NFA of about 5 billion baht,
ceteris paribus. On the other hand, the CPI variable
is the least sensitive to changes in the leading indicator. The leading indicator for Thailand is highly
persistent, with an autoregressive coefficient equal
to 0.91. In the crisis state, the volatility of the leading indicator is about 256 times larger than in the
normal or noncrisis state.
For Indonesia, the private bank foreign liabilities
(PBFL) variable is the most sensitive to changes in
the country’s leading indicator. A one-unit increase
in the factor is associated with a monthly increase
in PBFL of 2.72 percent. The reserves variable, with
a factor coefficient of 0.47 percent, is not as sensitive
as other variables. The leading indicator for Indonesia
is somewhat persistent, with an autoregressive coefficient of –0.64. In the crisis state, the volatility of
the leading indicator is about 31 times greater than
in the noncrisis state.
For Korea, the NFA variable is the most sensitive
to changes in the factor; a one-unit increase in the
factor is associated with a monthly increase in NFA
of 212.39 billion won. As in Thailand, CPI is the
32

least sensitive series to the factor, with a factor
coefficient of about 0.30 percent. The leading indicator for Korea is highly persistent, with an autoregressive coefficient of 0.92. The volatility of the
leading indicator is about 364 times larger in the
crisis state than in the noncrisis state.
Table 2 shows that, for all three countries, the
leading indicator of currency crisis is negatively
correlated with exchange rates. That is, increases in
the level of the leading indicator are associated with
currency depreciation. The currency crises for all
countries are anticipated by the dynamic factor
behavior in state 0, that is, for the high-mean and
high-volatility regime.
Variables such as NFA, private bank credits from
the central bank, and PBFL are the most useful in
signaling speculative pressures and currency crises
in these three countries. Crises would also be anticipated with a smaller lead by internal macroeconomic fundamentals such as domestic credits, the
money supply, the CPI, or foreign reserves. This finding supports evidence that the currency crises across
these three countries are likely to have originated in

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

TA B L E 1
Maximum Likelihood Estimates: Dynamic Factor Model with Regime Switching
Thailand

Indonesia

Korea

α̂0

–0.0756
(0.2190)

α̂0

4.9982
(2.0785)

α̂0

0.2075
(0.7200)

α̂1

0.1243
(0.0550)

α̂1

1.8668
(0.3120)

α̂1

0.0950
(0.0340)

φ̂1

0.9133
(0.0384)

φ̂1

–0.6442
(0.1729)

φ̂1

0.9181
(0.0257)

σ̂ 2υ0

9.0228
(3.9717)

σ̂ 2υ0

28.5281
(10.9832)

σ̂ 2υ0

5.5735
(3.8378)

σ̂ 2υ1

0.0352
(0.0140)

σ̂ 2υ1

0.9139
(0.6835)

σ̂ 2υ1

0.0153
(0.0085)

P̂00

0.9277
(0.0680)

P̂00

0.8641
(0.1083)

P̂00

0.8835
(0.1352)

P̂11

0.9933
(0.0069)

P̂11

0.9810
(0.0188)

P̂11

0.9903
(0.0069)

2
σ̂ GW_
DC

0.5136
(0.0689)

2
σ̂ CH_
NFA

622.7890
(59.0376)

2
σ̂ CH_
PB

242.5626
(22.6699)

2
σ̂ CH_
CPI

0.1661
(0.0166)

2
σ̂ GW_
M1

4.2514
(0.9551)

2
σ̂ GW_
NFA

91.1092
(8.7597)

2
σ̂ GW_
PBFL

276.6354
(27.8780)

2
σ̂ GW_
RESV

38.3251
(3.6027)

2
σ̂ GW_
DC

2
σ̂ CH_
NFA

1.2626
(0.1473)
2431783.9236
(226865.0648)

2
σ̂ GW_
PB

52.4856
(4.9980)

2
σ̂ CH_
CPI

0.0797
(0.0093)

λ̂GW_ DC

1.0000
(Restricted)

λ̂GW_ M1

1.0000
(Restricted)

λ̂GW_ DC

1.0000
(Restricted)

λ̂CH_ NFA

–4.9412
(1.1487)

λ̂GW_ NFA

0.7839
(0.3134)

λ̂CH_ NFA

212.3871
(75.3244)

λ̂CH_ PB

2.6914
(0.6788)

λ̂GW_ PBFL

2.7161
(0.5460)

λ̂GW_ PB

1.6953
(0.3660)

λ̂CH_ CPI

0.2096
(0.0191)

λ̂GW_ RESV

0.4683
(0.1793)

λ̂CH_ CPI

0.2951
(0.0205)

Note: The sample period is 1980:01–1999:06. Asymptotic standard errors (computed numerically) appear in parentheses. The factor
mean for crisis state is µ̂0 = α̂ 0 /(1– φ̂1), and for off-crisis state it is µ̂1 =α̂1 /(1– φ̂1).

TA B L E 2
Correlation of Factor with Exchange Rate and Leading Indicators
Thailand

N$BAHT
GW_DC
CH_NFA
CH_PB
CH_CPI

Indonesia

–0.6471
0.7845
–0.6530
0.5089
0.2304

N$RUPIAH
N$BAHT
GW_M1
GW_NFA
GW_PBFL
GW_RESV

Korea
–0.4762
–0.3022
0.8823
0.2171
0.4600
0.1911

N$WON
N$BAHT
GW_DC
CH_NFA
GW_PB
CH_CPI

–0.7083
–0.4076
0.4401
0.0184
0.4148
0.6318

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 7
Thailand: Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered probability of currency crises

Filtered dynamic factor
8

1.0
97:07–98:01

97:07–98:01

6
0.8

84:11–85:03

81:05–81:07

Pr o b a b ilit y

In d e x

4
2
0
81:05–81:07

0.6
0.4

–2

84:11–85:03

0.2
–4
–6
1980

1983

1986

1989

1992

1995

0.0
1980

1998

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database and model results

their respective private financial sectors and monetary sectors as a result of unsustainable financial
liberalization policies.
For Thailand, in particular, acceleration in the
growth rate of domestic credits and increases in the
level of private bank credits from the central bank
and in the level of the CPI led to increases in the
leading indicator of currency crises. Hence, pressures to devalue Thailand’s baht are associated with
increases in the dynamic factor and with decreases
in the level of NFA. For Indonesia, acceleration in
the growth rate of money, NFA, PBFL, and reserves
are associated with increases in the factor and,
therefore, with the devaluation of Indonesia’s rupiah.
For Korea, acceleration in the growth rate of domestic credits and private bank credits from the central
bank and increases in the level of NFA and the CPI
are associated with increases in the factor and,
hence, with the devaluation of Korea’s won.
Probabilities of currency crises. Figure 7 plots
the dynamic factor (the leading indicator) and the
probability of currency crises for Thailand. The leading indicator is quite stable for most of the sample
except for the periods prior to the currency crises in
1981:05 and 1997:07, when the factor moves up and
down considerably. This pattern can also be observed
in the probability of currency crises, which increases
substantially in 1981:02 (three months before the
1981:05 currency crisis) and in 1997:01 (six months
before the 1997:07 crisis). The factor is less sensitive
to the depreciation in 1984:11, when Thailand’s
authorities abandoned the fixed exchange rate vis-àvis the dollar. The economy displayed stronger fundamentals during this time and was less susceptible to
external shocks.
34

Figure 8 plots the dynamic factor and the probability of currency crises for Indonesia. Both are
quite stable, with values close to 0 for most of the
sample except around the currency crises. In fact,
they display abrupt oscillations in 1986–87,
1989–91, and 1997–98, anticipating the crises. In
particular, the factor and probability of currency
crises signal the currency crises in 1986:09 and in
1997:08 nine months in advance. On the other
hand, the devaluation in 1983:04 was very small.
This pattern is also reflected in the probability of
currency crises, which indicates weak speculative
pressure (around 2 percent in 1982:12). The small
probability of currency crises at the end of 1982
reinforces the view that the 1983 devaluation did
not originate from strong pressures from the financial sector and was mostly unanticipated. The
devaluation in 1986 was much larger in comparison,
and the probability of currency crises—ranging
from about 11 percent in 1986:06 to 58 percent
in 1986:09—gives clear signals of it, indicating
stronger speculative pressure. The 1997 devaluation was the most severe one experienced by
Indonesia (see Figure 2). The probability of currency crises ranged from 19 percent in 1996:11 to
60 percent in 1997:01—seven months prior to the
crisis in 1997:08. After the onset of the crisis, the
probability, ranging from 15 percent in 1997:10 to
almost 100 percent in 1998:01, indicated continuous speculative pressure.
One should note that the probability increased
substantially between 1989:07 and 1991:04. During
this period Indonesia underwent financial liberalization and experienced fluctuations in capital inflow
(a deceleration in portfolio and other short-term

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 8
Indonesia: Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered dynamic factor

Filtered probability of currency crises

1.0
97:08–98:12

0.8

86:09–86:10

Pr o b a b ilit y

10
In d e x

97:08–98:12

0.9

83:04

5
0

86:09–86:10

0.7
0.6
83:04

0.5
0.4
0.3
0.2

–5

0.1
0.0
1980

–10
1980

1983

1986

1989

1992

1995

1998

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database and model results

FIGURE 9
Korea: Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered dynamic factor

Filtered probability of currency crises

1.0
97:11–98:01

97:11–98:01

0.9

0.8
P ro b a b ility

In d e x

5
4
3
2

0.7
0.6
0.5
0.4

0.3

0.2

–1

0.1
0.0
1980

–2
1980

1983

1986

1989

1992

1995

1998

1983

1986

1989

1992

1995

1998

Source: Datastream, International Financial Statistics database and model results

flows and continued growth in foreign direct investment) while interest rates decreased significantly.
However, the exchange rates did not succumb to
the high speculative pressure in 1989:07–1991:04
because the government made a preemptive policy
response to structural changes in capital inflows
(see Radelet and Sachs 1998).
Figure 9 plots the dynamic factor and probability
of currency crises for Korea. Again, the dynamic factor series is quite stable except during the currency
crisis in 1997–98. The probability of currency crisis
reflects the speculative pressure and possible contagion from the crises in Thailand and Indonesia one
month earlier, in October 1997. When the depreciation of the Korean won occurred in November 1997,

the probability of currency crisis reached 100 percent. As the exchange rate fluctuation continued
into early 1998, the speculative pressure measured
by the probabilistic inference reached another peak
of 100 percent in 1998:02.

Out-of-Sample Results
n this section we examine the performance of
inferred probabilities in predicting currency
crises in an out-of-sample exercise. We compare and
evaluate the model performance of ex post forecasts with real-time ex ante forecasts using only
data available at the time of the forecast. The parameters were estimated using data up to 1997:01.
The in-sample estimates were then used to generate

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 10
Thailand: In-Sample and Out-of-Sample Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered dynamic factor

Filtered probability of currency crises

1.0
97:07–98:01

84:11–85:03

6
Pr o b a b ilit y

4
In-sample and out-of-sample
2

In d e x

97:07–98:01

0.8

Full

84:11–85:03

0
81:05–81:07

In-sample and out-of-sample
0.6
Full
0.4

–2
0.2
–4

81:05–81:07

0.0
1980

–6
1980

1983

1986

1989

1992

1995

1998

1983

1986

1989

1992

1995

1998

Note: In-sample data cover the 1980:01–1997:01 period; out-of sample data cover the 1997:02–1999:06 period. The full sample covers
the 1980:01–1999:06 period.
Source: Datastream, International Financial Statistics database and model results

FIGURE 11
Indonesia: In-Sample and Out-of-Sample Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered dynamic factor

Filtered probability of currency crises

1.0

P ro b a b ility

In d e x

0.8

In-sample and
out-of-sample

10
5
0

83:04

–5

83:04

Full
0.4
0.2
0.0
1980

–10
1983

86:09–86:10

0.6

97:08–98:12

86:09–86:10

1980

97:08–98:12

In-sample and
out-of-sample

Full

1986

1989

1992

1995

1998

1983

1986

1989

1992

1995

1998

FIGURE 12
Korea: In-Sample and Out-of-Sample Filtered Dynamic Factor and Filtered Probability of Currency Crises
Filtered dynamic factor

Filtered probability of currency crises
1.0

97:11–98:01

0.9

0.8

0.7

P r ob a bi lit y

In d e x

In-sample and out-of-sample

3
2
1

In-sample and out-of-sample

0.6
0.5

Full

0.4
0.3

Full

97:11–98:01

0.2
0.1

–1
–2
1980

1983

1986

1989

1992

1995

1998

0.0
1980

1983

1986

1989

1992

1995

1998

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

out-of-sample forecasts of the filtered probabilities and filtered dynamic factors. The out-of-sample
performance is analyzed for 1997:02–1999:06, which
is the period that includes the recent Asian currency crises.
The dynamic factor model with regime switching
successfully captures the crisis through the filtered
factor and filtered probability (see Figures 10, 11,
and 12). The out-of-sample filtered dynamic factors
based on data up to 1997:01 closely mimic the factors based on full-sample data up to 1999:06.
The filtered probability of currency crises for
Thailand based on information up to 1997:01 signals the country’s currency crisis in 1997:02, that
is, five months before the actual crisis occurred.
For Indonesia, the probability signals the crisis in
1997:01, seven months before the actual crisis. For
Korea, the probability signals a crisis in 1997:11,
coinciding with the actual crisis.

Conclusions
his article uses a dynamic factor model with
regime switching to construct leading indicators
of currency crises for Thailand, Indonesia, and
Korea. The analysis finds that most of the large currency depreciations in these countries during the
sample periods can be attributed in great part to the
deterioration of monetary and banking sector conditions, which was intensified by speculative pressures.
The dynamic factor model successfully produces
early probabilistic forecasts of the Asian currency
crises, particularly the most severe one, which
occurred in 1997. These results hold for both insample and recursive out-of-sample estimation.
This study demonstrates that the leading indicators of currency crises can be informative tools for
signaling future currency crises in real time and
could thus allow preemptive counterpolicy measures by the central bank.

REFERENCES
Bekaert, Geert, and Campbell R. Harvey. 1999. Chronology
of economics, political and financial events in emerging
markets. Columbia University, photocopy.

Hamilton, James D. 1989. A new approach to the economic
analysis of nonstationary time series and the business
cycle. Econometrica 57 (March): 357–84.

Burns, Arthur, and Wesley Mitchell. 1946. Measuring
business cycles. New York: National Bureau of Economic
Research.

Kim, Chang-Jin, and Charles Nelson. 1998. Business cycle
turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with
regime switching. Review of Economics and Statistics
80 (May): 188–201.

Chauvet, Marcelle. 1998. An econometric characterization
of business cycle dynamics with factor structure and
regime switching. International Economic Review 39
(November): 969–96.
Chauvet, Marcelle, and Fang Dong. 2002. A framework
for modeling country risk and financial crisis contagion.
Unpublished paper.

Perron, Pierre. 1989. The great crash, the oil price
shock, and the unit root hypothesis. Econometrica 57
(November): 1361–1401.
Phillips, Peter, and Pierre Perron. 1988. Testing for a unit
root in time series regression. Biometrika 75 (June):
335–46.

Dickey, David, and Wayne Fuller. 1979. Distribution of the
estimators for autoregressive time series with a unit root.
Journal of the American Statistical Society 74: 427–31.
Diebold, Francis X., and Glenn D. Rudebusch. 1996.
Measuring business cycles: A modern perspective. Review
of Economics and Statistics 78 (February): 67–77.

Radelet, Steven, and Jeffrey Sachs. 1998. The onset of
the East Asian financial crisis. NBER Working Paper
No. 6680, August.
Sachs, Jeffrey, Aaron Tornell, and Andres Velasco. 1996.
Financial crises in emerging markets: The lessons from
1995. NBER Working Paper No. 5576, May.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

Decomposing Inflation
ANDREW BAUER, NICHOLAS HALTOM, AND WILLIAM PETERMAN
Bauer and Haltom are senior economic analysts in the macropolicy section of the
Atlanta Fed’s research department. Peterman is an economic analyst with The Brattle
Group. The authors thank Robert Cage and Mary Lynn Schmidt for invaluable help with
BLS methodologies and Juan Rubio-Ramírez and Ellis Tallman for helpful comments.

ecent declines in U.S. core inflation
measures have prompted a renewed
effort to understand inflation dynamics. Since late 2001, core consumer
inflation rates have declined to levels
not seen since the early 1960s. Core
inflation as measured by the consumer price index
(CPI) declined to 1.1 percent (year-over-year) by
the end of 2003 while the core personal consumption expenditures price index (PCEPI) moved
below 1 percent. This decline in measured inflation
rates, coupled with uncertainty about future
demand conditions, generated concern and debate
among analysts and policymakers about near-term
inflation prospects. That concern was reflected in
the May 2003 Federal Open Market Committee
(FOMC) statement: “The probability of an unwelcome substantial fall in inflation, though minor,
exceeds that of a pickup in inflation from its already
low level.”
As core consumer inflation rates have edged
lower, an increasing and probably undue amount of
attention is being placed on the most recent observation. An aggregate inflation rate is limited in the
information it provides, especially with regard to
the sources of its movements. It is generally difficult
to know whether changes in aggregate inflation
result from broad-based price changes or from price
changes in only a few components. There may be
instances in which significant but otherwise idio-

syncratic relative price changes among a few underlying components drive movements in the aggregate inflation rate for a sustained period of time.
Analysts often attempt to confront this issue by
looking at price changes of major components and
making inferences about the impact of those
changes on the aggregate inflation rate. However,
these inferences are imprecise and do not provide a
complete accounting of aggregate inflation. A more
rigorous approach is to provide a precise decomposition of the inflation rate.
In this article we take the latter, more rigorous
approach.1 We calculate and plot the percentage
point contributions of major consumer expenditure
categories to core inflation measures over time.
This technique provides a wealth of information
concerning aggregate inflation behavior in a concise
way, enabling us to describe the composition of
inflation at any point in time. By highlighting the
composition of aggregate inflation, we gain greater
insight into the underlying trends in inflation and
are able to make more informed inferences about
the direction of inflation in the near term. A particularly important benefit of this method is that it
allows us to distinguish broad-based changes in
inflation from changes due to relative price movements of a few components.
Using this approach to examine long-run trends
in core inflation, our analysis finds that the primary
contributor to core inflation over the last two

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

decades has been core services. As core services
inflation has moved lower over this period, its composition has been relatively stable, with contributions of major components moderating uniformly. In
contrast, core goods inflation experienced a distinct
downward shift in the early 1990s, marked by a dramatic change in its composition. From examining
the long-term trends in the composition of core services and core goods inflation, we believe low inflation will likely persist in the near term.
Short-term movements in core services and core
goods inflation largely reflect relative price changes
of a few components. These relative price changes
are generally not persistent enough to drive sus-

From examining the long-term trends in the
composition of core services and core goods
inflation, we believe low inflation will likely
persist in the near term.

tained movements in the aggregate inflation rate.
However, in 2002 and 2003, we conclude, movements in core inflation mostly resulted from significant relative price changes of two components that
were persistent enough to alter the path of core
inflation for a sustained period.

Methodology
his analysis examines the two most widely followed measures of consumer inflation, the
consumer price index (CPI) and the personal consumption expenditures price index (PCEPI). More
specifically, the analysis focuses on the core components of these two measures. The core measures are
preferable because they strip out the more volatile
food and energy components. While large, persistent
movements in food and energy prices may represent
important relative price changes, these movements
could potentially mask other important price changes
that we are more interested in identifying.
Our approach follows the methodologies of the
Bureau of Labor Statistics (BLS) and the Bureau of
Economic Analysis (BEA) to calculate contributions for both CPI and PCEPI inflation, respectively.2
A contribution is the amount in percentage points
of the aggregate inflation rate that is attributed to a
particular component.3 We use the following general
formula for the CPI:4

 ( β Xi, t *βWi ) −( β Xi, t −1*βWi ) 
Ci, t = 100 * 

 ( β XI, t *βWI ) −( β XI, t −1*βWI )
 ( X − XI, t −1 )
*  I, t
,
 ( XI, t −1 ) 
where β Xi,t is the price index of the component i in
period t based to the reference period β; β XI,t is the
price index of the aggregate I in period t based to
the reference period β; βWi is the relative importance of the component i at the reference (or base)
period β; and βWI is the relative importance of the
aggregate I at the reference period β.5
For the PCEPI we use the general formula
 [ q + (q / Q F )]*( p − p )
i, t
t
i, t
i, t −1
Ci, t = 100 *  i, t −1
,
 ∑ j [ qj, t −1 + ( qj, t / QtF )]* pj, t −1 



(

)

where qi,t is the chained-dollar quantity of the component i in period t, pi,t is the chain-type price
index of the component i in period t, Q F is the
Fisher quantity index for the aggregate in period t
relative to period t – 1, and the subscript j includes
all the components of the aggregate.
As the formulas suggest, the magnitude of the
contribution of a particular component reflects its
change in price and its relative share, or weight, in
the aggregate. The sum of contributions of all components equals the aggregate inflation rate at any
point in time. For the purposes of this article, contributions of individual goods and services are
aggregated into major consumer expenditure categories, such as transportation goods, recreation services, and information processing equipment.6
We first decompose aggregate core inflation into
its contributions of core services and core goods.7
Then, to obtain greater detail on the underlying
trends and recent movements in core inflation, we
analyze separately the contributions of major components to core services inflation and core goods
inflation. We use the PCEPI to examine the composition and underlying trends of core inflation over
the long term. For our short-term focus on the
recent past, we use the CPI because it garners the
most attention from analysts and markets.8 It is
important to note that the BEA relies heavily on CPI
series in the construction of its price indexes.
Consequently, we believe our analysis of the longterm trends in the PCEPI applies to the CPI as well.

Contributions to Core Inflation
igures 1 and 2 show core inflation broken down
into its contributions of core services and core

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 1
Contributions to PCEPI Core Inflation
7

6
Core PCEPI (year/year)

Percentage points

3
Core services
2

0
Core goods
–1
1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Source: PCEPI core inflation rate, BEA; contributions calculated by the authors

goods for the PCEPI and CPI, respectively. Both figures clearly indicate that core services is the principal
contributor to overall core inflation. This prominence
reflects both its larger weight and rate of price
increase relative to core goods.9 The figures also indicate that the contribution of core goods eventually
turned negative. But note in Figure 1 that the contri-

bution of core goods in the PCEPI turned negative as
early as 1995. Core goods in the CPI did not turn negative until 2001. Figure 1 brings to light the point that
weakness in core goods prices is not as recent a phenomenon as commonly thought.10 Focusing on recent
inflation movements in Figure 2 shows that CPI core
inflation peaked in November 2001 at 2.8 percent and

1. For a more extensive treatment of the issues in this article, see Bauer, Haltom, and Peterman (forthcoming).
2. For the PCEPI, the methodology is derived from formulas in BEA (2001). For the CPI, the methodology is derived from
information in BLS (1997) and from conversations with BLS staff members.
3. The BLS refers to contributions to percent change for the CPI as “effects” although these effects are not published. The BEA
does not publish contributions to percent change for the PCEPI. However, the BEA does publish contributions to percent
change for the gross domestic product (GDP) and gross domestic purchases price indexes.
4. We modify the general formulas for both the CPI and PCEPI to account for contributions to year-over-year price changes.
All year-over-year price changes and contributions for the CPI are calculated using data that are not seasonally adjusted,
consistent with BLS reporting procedures.
5. From 1998 to 2001, the BLS uses 1993–95 base period relative importances. From 2002 to 2003, the BLS uses 1999–2000
base period relative importances.
6. See Appendix 1 for a detailed description of how we constructed these categories.
7. The CPI splits items into commodities and services while the PCEPI splits items into categories of goods and services.
For the sake of consistency, commodities in the CPI will be referred to as goods.
8. The PCEPI is a methodologically consistent index—that is, it revises historical data when there is a change in methodology.
The CPI, however, does not revise history when new methodologies are introduced. This distinction was a primary factor
considered in choosing to focus on the PCEPI for long-term trend analysis. In addition, the comprehensive change in the
structure of the CPI in 1998 complicates calculating contributions before 1998.
9. The nominal expenditure share of core services to core PCE has increased from 65 percent in 1983 to 70 percent currently.
The average rate of price increase for PCEPI core services from 1983 to 2003 is 3.8 percent, compared to 1.1 percent for
PCEPI core goods.
10. The decline in core goods prices in the CPI in late 2001 garnered much attention. The decline was easily identifiable because
core goods in the CPI is a published index. The decline in core goods prices that was exhibited much earlier in the PCEPI
may have been less perceptible because core goods is not a published index in the PCEPI.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 2
Contributions to CPI Core Inflation
3.5

3.0
Core CPI (year/year)

Core services

2.5

Percentage points

2.0

1.5

1.0
0.5

0.0

–0.5
Core goods
–1.0
1999

2000

2001

2002

2003

2004

Source: CPI core inflation rate, BLS; contributions calculated by the authors

FIGURE 3
Contributions to PCEPI Core Services Inflation
8

Rent
Recreation

Education
Household ops

Transportation
Personal

Other housing

Comm & info

Medical

Percentage points

5
Core services (year/year)
4

3
2

–1
1983

1985

1987

1989

1991

1993

Source: Calculated by the authors

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

1995

1997

1999

2001

2003

FIGURE 4
Contributions to CPI Core Services Inflation
5
Rent
Recreation
Other shelter

Transportation
Personal
Medical

Education
Household ops
Comm & info

Percentage points

Core services
(year/year)
3

–1
1999

2000

2001

2002

2003

Source: CPI core services inflation rate, BLS; contributions calculated by the authors

fell to 1.1 percent in December 2003.11 The contribution of core services fell 0.9 percentage point while
the contribution of core goods dropped 0.7 percentage point. The decline in the CPI core inflation rate
garnered a great deal of attention among the media,
analysts, and policymakers. Although there was some
dispute about its significance, many interpreted the
decline as an indication that the economy may be
headed toward overall price deflation.

Contributions to Core Services
aving identified core services as the primary contributor to core inflation, the analysis now turns
to the historical composition of core services inflation.
Figure 3 plots the PCEPI core services inflation rate
and the contributions of its major components from
1983 to 2003. Rent, medical care, and personal services are the primary contributors to core services
inflation. The long-run movements in rent and medical care largely determine the long-run trend in core

services inflation.12 The contribution of personal services exhibits sharp fluctuations over time, resulting
in short-run peaks and troughs in core services inflation. Outside of rent, medical care, and personal services, components’ contributions are relatively small
and stable over time. Overall, core services inflation
has slowed over the last two decades, with contributions of major components moderating uniformly.
We now turn our attention to the behavior of core
services inflation over the recent past. Recall from
Figure 2 that the disinflation in the overall core CPI in
2002 and 2003 resulted in part from a sustained moderation in the contribution of core services. To better
describe this movement, we plot the CPI core services inflation rate and its contributions from 1999 to
2003 in Figure 4.13 Most notably, the figure reveals
that the movement in CPI core services inflation was
almost entirely driven by rent during this period. The
contributions of other components were relatively
stable. Core services inflation fell from a peak of

11. The decline in the core CPI inflation rate over this period is 1.6 percentage points, rounded to one decimal place.
12. Over the 1983–2003 period, the correlation between the PCEPI core services inflation rate and the contribution of rent is
0.92, and the contribution of medical care services, 0.81.
13. Figure 4 displays some notable differences from Figure 3. In contrast to the PCEPI, rent is by far the largest contributor to core
services inflation in the CPI, with relatively small contributions coming from medical care and personal services. In the CPI, rent
has a much larger weight than in the PCEPI, while medical care and personal services have smaller weights. For a thorough
examination of the differences in the CPI and PCEPI as well as a detailed discussion of weighting issues, see Clark (1999).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE 5
Contributions to PCEPI Core Goods Inflation
4

3
Core goods (year/year)

Percentage points

–1
Alcohol
Recreation
Household furnishings

Transportation
Other
Medical care

–2
Education
Apparel
Info process
–3
1983

1985

1987

1989

1991

1993

1995

1997

1999

Source: Calculated by the authors

FIGURE 6
Contributions to CPI Core Goods Inflation
2.5

Alcohol
Recreation

2.0

Apparel
Info process

Education
Transportation

Household furnishings

Other
Medical Care

1.5

Percentage points

1.0
0.5
0.0
–0.5
–1.0

Core goods
(year/year)

–1.5
–2.0
–2.5
–3.0
1999

2000

2001

2002

Source: CPI core goods inflation rate, BLS; contributions calculated by the authors

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

2003

2001

2003

4.0 percent in February 2002 to 2.6 percent in December 2003, a decline of 1.4 percentage points. The
contribution of rent to core services inflation was
2.3 percentage points in February 2002 and fell
to 1.1 percentage points in December 2003, a decline
of 1.2 percentage points. Consequently, the decline
in core services inflation in the 2002–03 period did not
reflect broad-based disinflation. Instead, it reflected a
significant and persistent relative price change in rent.

Contributions to Core Goods
he shift in core goods over the past twenty years
from being a positive contributor to core inflation
to being a negative contributor reflects a number of
changes in major components that have dramatically
altered its long-run trend. We plot the PCEPI core
goods inflation rate and the contributions of its major
components from 1983 to 2003 in Figure 5.14 From
1983 through 1991, most components contributed
positively to core goods inflation. A number of relative
price changes drove short-term fluctuations in the
core goods inflation rate. However, these relative
price changes were often offsetting and were not persistent enough to significantly drive the core goods
inflation rate from its relatively flat trend.
In 1992 there was a distinct drop in the core
goods inflation rate, characterized by broad-based
downward movement in its composition (see
Appendix 2). Many components that contributed
positively before this shift began to either contribute less positively or began to contribute negatively. In addition, the negative contribution of
information processing equipment increased dramatically. From 1997 on, it is difficult to identify the
trend in core goods inflation mainly because the
core goods inflation rate dropped sharply from 2001
through 2003. However, the composition of core
goods has been relatively stable since 1997, suggesting that the core goods inflation rate has settled
to a lower, perhaps slightly negative long-run mean.
We noted in our discussion of Figure 2 that the
steepened decline in core goods prices in 2002 and
2003 was a significant factor in overall core disinflation. This movement is described in Figure 6, which
plots the CPI core goods inflation rate and its contributions from 1999 to 2003.15 From November

2001 to December 2003, the CPI core goods inflation rate fell from 0 percent to –2.5 percent. This
drop resulted from a less positive contribution of
other goods (largely tobacco) and increasingly negative contributions of household furnishings and
transportation. The largest contributor to the
decline was transportation. From November 2001
to December 2003 the contribution of transportation fell 1.4 percentage points, with used vehicles
accounting for 0.9 percentage point. The collective
contribution of other goods and household furnishings also fell 0.9 percentage point during this period.
Thus, the drop in core goods inflation resulted from
price declines in several components. Most notable

The decline in core services inflation in the
2002–03 period did not reflect broad-based
disinflation. Instead, it reflected a significant
and persistent relative price change in rent.

among these was the large price decline in used
vehicles (see the box).

Conclusion
n this article, we determine the precise impact of
major components on aggregate inflation measures. We calculate and plot the percentage point
contributions of major consumer expenditure categories to core inflation measures over time. This technique provides an information-rich picture of inflation
behavior, highlighting its composition and underlying
trends. By analyzing the composition of aggregate
inflation, we are able to make more informed inferences about the direction of inflation in the near term.
We are also able to distinguish broad-based changes
in inflation from changes in inflation due to relative
price movements of a few components.
We find that core services has been the primary
contributor to core inflation over the last two
decades. The composition of core services inflation is

14. Alcoholic beverages are not included in the BEA’s core PCEPI. In Figure 1, we presented the BEA core PCEPI without alcoholic beverages. However, in Figure 5, we include alcoholic beverages in our PCEPI core goods in order to be consistent with
CPI core goods, which does include alcoholic beverages.
15. Figure 6 shows the same pattern of contributions in CPI core goods as exhibited in the PCEPI during this period in Figure 5.
However, there are some differences in the magnitude of the contributions, reflecting the different weighting in the two
indexes. Most notably, the negative contribution of information processing equipment is considerably less for CPI core goods
because the CPI uses fixed expenditure weights from historical base periods. CPI core goods inflation did not turn negative
until late 2001 largely because of the smaller negative contributions of information processing equipment prior to 2002.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

BOX
Significant and Persistent Relative Price Changes: The Case of Rent and Used Vehicles

nalysts have widely discussed rent and used
vehicle prices as important factors in CPI
core disinflation during the 2002–03 period. But
the precise degree to which these components
were lowering core inflation was not clear. Our
analysis shows that, from November 2001 (the
date of the peak in CPI core inflation) to
December 2003, the contribution of rent to CPI
core inflation fell 0.8 percentage point while the
contribution from used vehicles dropped 0.3 percentage point—a total of 1.1 percentage points, a
considerable portion of the 1.6 percentage point
decline in CPI core inflation.
What would CPI core inflation have looked like
without these recent movements in rent and used
vehicles? As a counterfactual exercise, we construct a hypothetical CPI core inflation measure
assuming alternative rates of price change for
rent and used vehicles.1 We compare this alternative measure to the actual CPI core inflation rate
in Figure A. In the counterfactual index, we hold
the rates of inflation of rent and used vehicles
constant from November 2001 through December
2003.2 In contrast to the steep decline in the actual
CPI core inflation rate, our constructed measure
of core inflation shows relatively moderate disinflation over the past two years. This exercise indicates that the decline in actual CPI core inflation
reflected significant, persistent relative price
changes of rent and used vehicles, not broadbased disinflation. We further argue that these
price changes reflect not a fundamental weakening in housing and vehicle demand but, instead,
the dynamic effects of interest rates on consumer
demand for substitutes.

relatively stable over time and largely driven by
movements in a few major components. The story is
quite different for core goods inflation. The composition of core goods inflation has changed dramatically
over time, resulting in a distinct downward shift in
the core goods inflation rate in the early 1990s.
Trends in the composition of core inflation lead
us to believe that low inflation will likely continue
to persist in the near term. The relative stability in
the composition of core services inflation suggests
little change, in either direction, in the aggregate
core services inflation rate. The composition of
core goods inflation suggests that core goods defla-

FIGURE A
CPI Core Inflation

Percent change (year/year)

3.5

Holding rent & used vehicles
inflation constant after Nov. 2001

3.0
2.5
2.0
1.5
Actual

1.0
0.5
0.0
1999

2000

2001

2002

2003

Source: Actual CPI core inflation rate, BLS; counterfactual, authors

Rent
Downward pressure on rental prices mainly
resulted from an increase in demand for homeownership, which was spurred by historically
low mortgage interest rates (see Figure B). As
housing starts and home sales surged in the
recent recession and recovery, the national rental
vacancy rate jumped from 7.8 percent in the
fourth quarter of 2000 to 10.2 percent in the
fourth quarter of 2003. This effect was compounded by the way owner-occupied housing
prices are measured in the CPI. The CPI uses a
rental-equivalence approach, measuring the
value of the shelter services an owner receives
from his or her home. Price movements in owners’ equivalent rent reflect changes in prices of

tion will likely continue into the near term. There
have been significant changes in market structure,
trade patterns, productivity growth, and price measurement that suggest continued downward pressure on goods prices going forward. At the same
time, it is not obvious to us that the decline in
goods prices will accelerate. The general stability
in the composition of core goods inflation since
1997 suggests that the core goods inflation rate will
rather revert to a moderately negative rate of
decline. With stable core services inflation and stable core goods deflation, we expect that overall
core inflation will remain low.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

FIGURE B

FIGURE C

Owners' Equivalent Rent and
Mortgage Interest Rates

Used Vehicle Prices and
New Auto Finance Rates
9.5
9.0

CPI owners' equivalent
rent (left axis)

4.0

8.5
8.0

3.0

7.5

2.5

7.0
6.5

30-year fixed mortgage rate,
lagged 20 months (right axis)

1.5
1996

2000

–4

–8

2002

Source: BLS; Federal Home Loan Mortgage Corporation

4
CPI used vehicles
(left axis)

–12
1996

6.0
1998

Auto finance company interest rates on new
car loans, not seasonally adjusted (right axis)

Percent

3.5

2.0

Percent change (year/year)

4.5

Percent

Percent change (year/year)

5.0

2
1998

2000

2002

Source: BLS; Federal Reserve Board

rental units that are comparable in characteristics
to owner-occupied homes. Therefore, increased
demand for homeownership put downward
pressure not only on tenants’ rent but also on
owners’ equivalent rent—the largest component
in the CPI.
Used Vehicles
The decline in prices of used vehicles largely
reflected an increase in demand for new vehicles in
response to record-low financing and rebate offers

(see Figure C). Used vehicle prices in the CPI are
derived from wholesale auction prices. The surge
in demand for new vehicles increased the supply
of used autos in the wholesale market while also
decreasing dealers’ demand for used autos.
According to Manheim Auctions, a leader in the
used vehicle wholesale auction market, its used
vehicle value index (manheimvalueindex.com)
fell 5.3 percent between November 2001 and
December 2003. Manheim has cited new vehicle
incentives as a primary contributor to this decline.

1. We do not exclude rent and used vehicles from our alternative index because doing so would significantly alter the
consumer basket, in effect redistributing the weights of these two components to the remaining components.
2. The November 2001 inflation rate was 4.7 percent for tenants’ rent, 4.4 percent for owners’ equivalent rent, and –1.2 percent for used vehicles.

We note that short-term movements in core
services and core goods inflation largely reflect
relative price change of a few components. These
relative price changes are generally not persistent
enough to cause the aggregate inflation rate to
deviate considerably from its perceived trend.
However, for 2002 and 2003, we conclude that
movements in core inflation mostly resulted from
two significant relative price changes—the moderation in the increase of rental prices and the
decline in used vehicle prices—that were persistent enough to alter the path of core inflation for
a sustained period.

From November 2001 to December 2003, the
contribution of rent to core CPI inflation fell 0.8
percentage point while the contribution of used
vehicles dropped 0.3 percentage point—totaling 1.1
percentage points. The core CPI inflation rate over
this period declined 1.6 percentage points. Absent
the movements in these two components, core disinflation over the past two years has been very moderate. These results suggest that the concern and
discussion regarding overall price deflation were
perhaps overstated. Moreover, our results highlight
the importance of gauging the impact of relative
price changes in a low-inflation environment.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

APPENDIX 1
Constructing Major Components for PCEPI and CPI Core Goods and Core Services

he BEA publishes only the aggregate core
PCEPI, not indexes for core goods and core
services. We create these indexes to obtain comparable measures to the core commodities and
core services series of the CPI. Within core
goods and core services, we aggregate the
PCEPI series to create major components comparable to the breakdown of major components
in the CPI. The BEA does not include alcoholic
beverages in core PCEPI, but we include them in
Figure 5 to be consistent with the BLS definition
of core CPI.

PCEPI Core Goods

• Alcoholic beverages
• Household furnishings: semidurable furnishings; cleaning, light supplies, and miscellaneous
paper products; flowers, seeds, and potted
plants; furniture, mattresses and bedsprings,
and kitchen and other household appliances;
china, glassware, tableware, and utensils; and
other durable house furnishings
• Apparel: clothing and shoes and jewelry and
watches
• Transportation: motor vehicles and parts
• Medical care: drug preparations and sundries
and ophthalmic and orthopedic equipment
• Recreation: toys and sports equipment; magazines and newspapers; audio, video, and musical
instruments; sports, photographic equipment,
and cycles; and boats and aircraft
• Education: books and maps
• Information processing: computers, peripherals,
and software
• Other: tobacco, toilet articles and preparations,
stationery and writing supplies, and expenditures abroad by U.S. residents
CPI Core Goods

• Alcoholic beverages
• Household furnishings: window and floor coverings and other linens; furniture and bedding;
appliances; other household equipment and furnishings; tools, hardware, outdoor equipment,
and supplies; and housekeeping supplies
• Apparel
• Transportation: new vehicles, used cars and
trucks, and motor vehicles parts and equipment
• Medical care: medical care commodities
• Recreation: televisions; other video equipment,
videocassettes, and discs; audio equipment,
audio discs, tapes, and other media; pets and
pet products; sporting goods; photographic
48

equipment and supplies; other recreational
goods; and recreational reading materials
• Education: educational books and supplies
• Information processing: personal computers
and peripheral equipment, computer software
and accessories, and other information processing equipment
• Other: tobacco and smoking products, personal
care products, and miscellaneous personal goods
PCEPI Core Services

• Rent: owner-occupied nonfarm dwellings, space
rent; tenant-occupied nonfarm dwellings; and
rental value of farm dwellings
• Other housing: household insurance premiums,
household insurance benefits paid, and other
housing services
• Household operations: water and sanitary services, domestic services, moving and storage,
rug and furniture cleaning, electrical repair,
upholstery and furniture repair, and other
household operations
• Transportation: transportation services
• Medical care: medical care services
• Recreation: recreation services
• Education: private education and research
services
• Communication and information: telephone
and telegraph and postage
• Personal: personal care services, personal
business services, religious and welfare activities, and net foreign travel
CPI Core Services

• Rent: rent of primary residence and owners’
equivalent rent of primary residence
• Other shelter: lodging away from home and
tenants’ and household insurance
• Household operations: water and sewer trash
collection services and household operations
• Transportation: transportation services
• Medical care: medical care services
• Recreation: cable television; rental of videotapes and discs; pet services, including veterinary; photographers and film processing; and
recreation services
• Education: tuition and other school fees and
childcare
• Communication and information: telephone
services and computer information processing
services
• Personal: personal care services and miscellaneous personal services

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

APPENDIX 2
Examining the Shift in the Composition of Core Goods Inflation

he composition of PCEPI core goods inflation
has displayed a dramatic shift over the past
twenty years. Prior to 1992, the contributions to
core goods inflation were mostly positive. Since
that time, many components have become consistently negative contributors to core goods inflation.
Consequently, core goods inflation has fallen from
an average rate of 2.6 percent during the 1983–91
period to an average rate of –0.1 percent post 1991.
To understand this shift, we look at the magnitudes,
signs, and volatility of the contributions by component both within and across the pre- and postshift
periods. We then examine those components that
have changed most dramatically.
The table presents the average contribution, the
standard deviation, and the high/low contribution
for each major component within each period. The
table also shows the difference in the average contribution for each component across periods. We
see relatively large average contributions across
periods from apparel (0.44 and –0.30) and transportation (0.54 and 0.19) and increasingly large
negative contributions in the postshift period from
information processing (–0.55) and recreation
(–0.21).1 The most notable changes in sign across
periods are within apparel and recreation. The
most volatile components across periods, as mea-

sured by standard deviation of contributions, are
transportation (0.27 and 0.46) and other goods
(0.20 and 0.32). Apparel too is quite volatile in the
preshift period (0.38) although the volatility in contributions decreases notably in the 1990s (0.24).
There have been significant changes in market
structure, trade patterns, productivity growth, and
price measurement that have placed downward
pressure on goods prices in many components.
The components most affected have been apparel,
information processing equipment, recreation
goods, and transportation goods. The following
sections explore the impact of these changes.
Apparel

The change in apparel from positive contributor
to negative is not especially surprising. Significant
changes have occurred in the apparel industry
at both the manufacturing and retail levels. Most
apparel manufacturing has shifted abroad to lowcost producers, increasing the volume of apparel
imports. Since 1994 U.S. industrial production of
apparel has fallen by nearly 40 percent. At the retail
level, discount retailers have become more prominent in the industry. Both of these developments
have put downward pressure on apparel prices.
During the 1983–91 period, apparel prices grew

TABLE

Info
rma
tion
goo
ds
Oth
er
goo
ds

goo
ds
Edu
cat
ion

Rec
rea
tion

goo
ds

Tra
nsp
or t
atio
ng
ood
s
Me
dic
al g
ood
s

App
are
l

furn
ish
ing
s
Ho
use
hol
d

Alc
oho
l

PCE
P
goo I core
ds
infl
atio
n

A Breakdown of Contributions to PCEPI Core Goods Inflation

1983–91
Average growth/
contribution
Standard deviation
High
Low

2.65
0.63
3.81
1.23

0.33
0.17
0.83
0.16

0.34
0.13
0.84
0.12

0.44
0.38
1.15
–0.42

0.54
0.27
1.21
–0.03

0.39
0.07
0.54
0.29

0.07
0.12
0.29
–0.17

0.06
0.03
0.14
–0.01

–0.10
0.06
–0.01
–0.27

0.55
0.20
1.22
0.28

0.28
0.13
0.55
0.05

–0.21
0.21
0.22
–0.55

0.03
0.03
0.08
–0.09

–0.55
0.23
–0.14
–0.95

0.34
0.32
1.39
–0.30

–0.04

–0.45

–0.21

1992–2003
Average growth/
contribution
Standard deviation
High
Low

–0.08
0.99
2.47
–2.23

0.13
0.06
0.42
0.02

–0.03
0.21
0.37
–0.61

–0.30
0.24
0.43
–0.75

0.19
0.46
1.11
–0.74

1992–2003 period less 1983–91 period
Difference in average
growth/contribution

–2.73

–0.19

–0.37

–0.74

–0.35

–0.11

–0.28

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

A P P E N D I X 2 (continued)

1.8 percent on average, but during the 1992–2003
period, they fell 1.4 percent, subtracting 0.30 percentage point on average from core goods inflation.
Information Processing Equipment

The magnitude of the negative contributions
of information processing equipment is remarkable. The rapid pace of computer innovation and
the role of hedonic quality adjusting in contributing to price declines have been well documented.2
In addition, productivity gains have been especially strong among high-technology manufacturers, reducing production costs. On average, prices
of computers, peripheral equipment, and software
declined 14.5 percent in the 1983–91 period, and
this decline accelerated to 23.4 percent in the
1992–2003 period. At the same time, the nominal
expenditure share of computers, peripheral
equipment, and software to core goods personal
consumption expenditures rose from 0.4 percent
in January 1983 to 2.5 percent in December 2003.
Together, the steepened price decline and greater
nominal expenditure share resulted in a dramatic
increase in the magnitude of the average contribution over the two periods from –0.10 to –0.55.
Recreation Goods

The increasingly negative contribution from
recreation goods reflects a variety of factors, including quality adjustment of price indexes, the introduction of new products, and import competition.
Over the last several years, the BLS has introduced
hedonic quality adjustment procedures for many
consumer electronic goods, including televisions,
VCRs, DVD players, and audio equipment.3 In addition, the BLS changed its sampling procedures in
1998 to facilitate the introduction of new goods on
a more frequent basis (Cage 1996). Incorporating
items early in their product cycle captures the dramatic reduction in price that is often associated with
relatively new products. This change is particularly

relevant for consumer electronic goods. Import
competition has also put downward pressure on
recreation goods prices. Import prices for most
recreation goods began to fall considerably in the
mid 1990s—averaging –2.7 percent for home entertainment equipment and –0.8 percent for toys and
sporting goods. The increase in discount retailers
has also placed greater downward pressure on
recreation goods prices.
Transportation Goods

The contribution of transportation goods prices
is noteworthy in that it is large in magnitude,
exhibits considerable volatility, and turns negative
in the mid-1990s. Expenditures on motor vehicles
and parts are a relatively large share of total core
goods consumption, averaging 23 percent of core
goods personal consumption expenditures from
1983 to 2003. Prices fluctuate considerably, especially for used vehicles, resulting in large swings in
contributions to core goods inflation. Since 1983,
the average year-over-year price change for used
autos was 3.4 percent, with a standard deviation
of 7.0 percent. In addition, motor vehicle prices
shifted downward in the mid-1990s. Before 1995,
price changes for motor vehicles and parts averaged 2.8 percent. Since 1995, they have averaged
just 0.3 percent. This shift in prices reflects the
changing structure of the motor vehicle industry.
Since 1996, the share of domestic light vehicle
sales to total light vehicle sales has fallen by nearly
10 percentage points. Domestic vehicle manufacturers’ attempt to retain market share has placed
downward pressure on new vehicle prices.
Meanwhile, leasing has increased dramatically
over the past decade, resulting in an influx of latemodel, low-mileage used cars into the vehicle
market. In essence, leasing has produced a new
category of used cars that is a closer substitute for
new vehicles. Used car superstores have emerged,
increasing competition at the retail level.

1. “Other” goods also has a large contribution across periods. The magnitude and volatility of this contribution mostly
reflect large price swings in tobacco goods.
2. Beginning in 1991, the BEA used the BLS producer price index (PPI) series for electronic computers, which adjusts for
quality changes in computers. Once the BLS began using hedonic quality adjustment for computers in the CPI in 1998,
the BEA switched to the CPI series. For a good discussion and example of hedonic quality adjustment for computers,
see Holdway (2000).
3. Hedonic quality adjustments were incorporated for televisions in January 1999 and for VCRs and DVD players in April
2000. The impact of hedonic pricing does not necessarily translate to a downward adjustment to the published (nonadjusted) index. Liegey (1994) and Liegey and Shepler (1999) show that the introduction of hedonic pricing for apparel
and VCRs, respectively, did not greatly affect the price changes. However, Moulton, LaFleur, and Moses (1998) show
that the introduction of hedonic pricing of televisions did result in a downward adjustment.
50

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

REFERENCES
Bauer, Andrew, Nicholas Haltom, and William Peterman.
Forthcoming. Examining contributions to core consumer
inflation measures. Federal Reserve Bank of Atlanta
working paper.

Liegey, Paul R., and Nicole Shepler. 1999. Adjusting VCR
prices for quality change: A study using hedonic methods.
Monthly Labor Review 122 (September): 22–37.

Cage, Robert. 1996. New methodology for selecting CPI
outlet samples. Monthly Labor Review 119 (December):
49–61.

Moulton, Brent R., Timothy J. LaFleur, and Karin E. Moses.
1998. Research on improved quality adjustment in the CPI:
The case of televisions. Presented at the Conference of
the Ottawa Group, April.

Clark, Todd E. 1999. A comparison of the CPI and the
PCE price index. Federal Reserve Bank of Kansas City
Economic Review 84 (Third Quarter): 15–29.

U.S. Bureau of Economic Analysis (BEA). 2001. A guide
to the NIPA’s methodology, National Income and
Product Accounts, 1929–97. Washington, D.C.: BEA.

Holdway, Michael. 2000. Quality-adjusting computer
prices in the producer price index: An overview. Bureau
of Labor Statistics. <www.bls.gov/ppi/ppicomqa.htm>
(February 17, 2004).

U.S. Bureau of Labor Statistics (BLS). 1997. The consumer
price index. Chap. 17 in BLS Handbook of Methods.
Washington, D.C.: BLS.

Liegey, Paul R. 1994. Apparel price indexes: Effects of
hedonic adjustment. Monthly Labor Review 117 (May):
38–45.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W First Quarter 2004

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