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FEDERAL RESERVE BANK OF CLEVELAND

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POLICY DISCUSSION PAPERS

Does Wage Inflation
Cause Price Inflation?
by Gregory D. Hess and Mark E. Schweitzer

FEDERAL RESERVE BANK OF CLEVELAND

Does Wage Inflation
Cause Price Inflation?
by Gregory D. Hess and Mark. E. Schweitzer

Recent attention has turned from unemployment levels to wage growth as an indicator of imminent inflation. But is there any evidence to support the assumption that increased wages cause
inflation? This study updates and expands earlier research into this question and finds little support for the view that higher wages cause higher prices. On the contrary, the authors find more

Gregory D. Hess is the
Danforth–Lewis Professor of
Economics at Oberlin College and
an academic consultant to the
Federal Reserve Bank of Cleveland.
Portions of this text were prepared
for his fall 1999 statement to the
Shadow Open Market Committee.
Mark E. Schweitzer is an economist
at the Federal Reserve Bank of
Cleveland. The authors would like
to thank Allan Meltzer and Charles
Plosser for very helpful comments.

evidence that higher prices lead to wage growth.

Materials may be reprinted if the
source is credited. Please send
copies of reprinted materials to
the editor.
We invite questions, comments,
and suggestions. E-mail us at
editor@clev.frb.org.

POLICY D I S C U S S I O N PAPERS

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ISSN 1528-4344

FEDERAL RESERVE BANK OF CLEVELAND

“Unions Seek Big Pay Gains, Sparking Inflation Worries”
Headline, Wall Street Journal, September 3, 1999.

Introduction
Wall Street economists and journalists have frequently focused on labor market activities to
help foretell inflationary price pressures. Earlier in the current expansion, some considered
a low unemployment rate (below 6.5 percent) a harbinger of rising inflation. The com
monly held view was that if the aggregate demand for goods and services caused unem
ployment to fall below some “natural” rate, inflation would accelerate. However, although
the unemployment rate continued to fall throughout the 1990s, inflation never rose. This
led economists to reconsider whether the “threshold” unemployment rate, termed the natural or nonaccelerating rate of inflation (NAIRU), had fallen from approximately 6.5 percent to 4.5 percent. Recent work by Staiger, Stock, and Watson (1997), however, suggests
that even a time-varying natural rate of unemployment is not a very useful tool for predicting inflation. 1
With uncertainty about the unemployment rate’s reliability as an early-warning device
for rising inflation, recent attention has turned to wage and compensation growth for a
labor market indicator of inflation. The standard argument for how increased labor costs
tend to push up prices, leading to the wage-price spiral, is as follows:

“… when buoyant demand reduces unemployment (at least relative to recent experi-

1. Stock and Watson (1999) find that
unemployment does not help in
forecasting U.S. inflation, but that
an indicator of aggregate demand
does. However, in contrast to the
research we present in this paper,
they do not explore issues of
simultaneity, measures of productivity, or unit labor costs in their
forecasts.

enced levels), inflationary pressure develops. Firms start bidding against each other for
labour, and workers feel more confident in pressing wage claims. If the inflationary pressure is too great, inflation starts spiraling upwards: higher wages lead to higher price
rises, leading to still higher wage rises, and so on. This is the wage-price spiral.”2

2. Layard, Nickell, and Jackman
(1994), pp. 11.

Surprisingly, the recent shift in attention to higher wages as the cause of higher prices
leaves unexplained the problem of how wages get high in the first place.
The intuition behind this view is that since labor costs are a large fraction of a firm’s
total costs of production, rising wages and compensation should put pressure on firms to
pass these higher costs on as higher prices. We have several reasons to doubt the accuracy of this view. First, if a wage increase is brought about by increased labor productivity,
it will not create inflationary pressure.3 Second, a wage increase will not create inflationary
pressure if it leads to a squeeze in profits because a firm cannot pass along cost increases.
No firm inherits the right to simply “mark-up” the prices of its output as a constant proportion above its costs; competitive market pressures strongly influence the pricing deci-

3. Indeed, wage increases supported
by productivity growth should not
be considered “wage inflation” at
all, but we will occasionally use
this phrase as it is in the existing
literature.

sions of firms. Finally, causation could work in the opposite direction: An increase in aggregate demand may permit firms to raise the price of their products, and the resulting
increase in profits would lead workers to demand higher wages in future negotiations.

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P O L I C Y D I S C U S S I O N PA P E R S

NUMBER 1, APRIL 2000

It turns out that the vast majority of the published evidence suggests that there is little reason to believe that wage inflation causes price inflation. In fact, it is more often
found that price inflation causes wage inflation. Our recent research, which updates and
expands on the current literature, also provides little support for the view that wage gains
cause inflation. Moreover, wage inflation does a very poor job of predicting price inflation throughout the 1990s, while money growth and productivity growth sometimes do a
better job. The policy conclusion to be drawn is that wage inflation, whether measured
using labor compensation, wages, or unit-labor-costs growth, is not a reliable predictor of
inflationary pressures. Inflation can strike unexpectedly without any evidence from the
labor market.

Data
Before exploring the econometric evidence on this debate, we provide a plot of price inflation (as measured by the growth rate of the personal consumption expenditure deflator)
and wage inflation (as measured by the growth of nonfarm business compensation) in figure 1.4 The series are graphed over the time period 1960:IQ to 1999:IIIQ. Although the

4. The appendix provides a full
.
description of each series.

growth in wages fluctuates more wildly than does inflation, both series generally share the
same pattern (they have a correlation coefficient of 0.408): Both trended upwards throughout the 1960s and 1970s, they both peaked and tended to decline throughout the 1980s,
and then they stabilized during the 1990s. Obviously, wages and price changes are related, but the direction of causation isn’t readily apparent.
As mentioned above, however, the growth of nominal wages may be a poor measure
of cost pressures faced by firms, since if wage growth is driven by productivity growth,
then firms will not have to pass higher wages on as higher prices. This argument also would
require that employers compensate workers for productivity gains; thus, productivity should
be accounted for when asking if wages are driven by inflation. Fortunately, the Bureau of
FIGURE 1

G R O W T H O F U N I T L A B O R C O S T S A N D P R I C E I N F L AT I O N

Annualized growth rates
16
12
Price inflation
8

4

0

–4
Growth in ULC
–8
1959

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

2

FEDERAL RESERVE BANK OF CLEVELAND

Labor Statistics also reports unit labor costs, that is, the labor cost to the firm of producing
one unit of output, which adjusts wages for labor productivity. Figure 2 charts price inflation and the growth in unit labor costs for 1960:IQ to 1999:IIIQ. One can see that while
unit labor costs are quite volatile, they tend to track inflation closely (the two series have
a correlation coefficient of 0.642), but causation is no clearer.

Econometric Literature
While the raw data show that measures of wages and prices move strongly together, the
academic literature is divided as to whether there is empirical evidence that wages cause
prices. To be clear, academic economists use the term causality as in “Granger-causality.”
The test for Granger-causality involves examining whether lagged values of one series (say
wages) have significant in-sample explanatory power for another variable (say prices). Of
course, both variables may “Granger-cause” one another, in which case one can conclude
only that both economic series are determined simultaneously; hence, a researcher cannot
conclude that one series has an independent causal effect on the other. The matter
becomes even more complicated if the series in question are “cointegrated,” which is the
case if the levels of the series move together over the long run, even though the individual series are best modeled in growth rates. In this case, the researcher must be careful to
include “error correction terms” in the Granger-causality tests to allow the series to catch
up with one another.5 The significance of the error correction terms in the Granger-causality test simply reflects the fact that the series in question are driven to return to a long-run
equilibrium relationship that is noncausal.
Furthermore, researchers’ conclusions about the causal effects between wages and

5. The omission of these error
correction terms in a Grangercausality test specified in growth
rates would lead to a standard
omitted variable bias in the test
for Granger-causality.

prices often depend on the sample length, the number of explanatory variables used
(including the number of lags of each variable), and the particular measure of prices used.
Two recent papers typify the disagreement. Mehra (1993) examines a system of variables
that includes inflation, the output gap, and unit labor costs as a measure of wages, as well

FIGURE 2

WA G E A N D P R I C E I N F L AT I O N

Annualized growth rates
14
12

Wage inflation

10
8
6
4

Price inflation

2
0
–2
1959

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

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POLICY DISCUSSION PAPERS

NUMBER 1, APRIL 2000

as dummy variables for wage and price controls and a measure of energy prices, for the
period 1956:IQ to 1992:IVQ in the United States. He reports that one can conclude that
wages Granger-cause inflation only when one uses the CPI to measure prices, and that one
obtains much stronger evidence that prices Granger-cause wages when one uses the more
general GDP price deflator. These results are consistent with earlier work by Mehra (1991),
Gordon (1988), and Huh and Trehan (1995). More recently, Ghali (1999) re-explores the
econometric issues in Mehra (1993) and modifies the system of variables to include the relative price of imported goods, although he shortens the sample period to
1959:IQ–1989:IIIQ and only considers prices as measured by the GDP price deflator. In
contrast to the findings of Mehra (1993), Ghali (1999) reports that there is strong evidence
that wages Granger-cause prices and advocates that the Federal Reserve should monitor
unit labor costs as a predictor of future inflation.
However, the findings by Ghali (1999) are atypical and not confirmed by the results
reported below. Three further studies find the systematic evidence that wages cause prices
woefully insufficient. First, exploring whether sectoral wage growth causes inflation, Rissman
(1995) concludes, “ ... In most of the industries examined, the direction of causality runs from
prices to wages rather than wages to prices. Only in manufacturing and retail trade does proTABLE 1

GRANGER-CAUSALITY TESTSa
Panel A. Wages Measured by Compensation per Hour with Productivity Included Separately
Are prices caused by
Time period
1960:IQ –1999:IIIQ
1980:IQ –1999:IIIQ

Wages
0.330
0.295

Productivity
0.339
0.163

Money
0.296
0.270

Error correction
0.054
0.742

Are wages caused by
Time period
1960:IQ –1999:IIIQ
1980:IQ –1999:IIIQ

Prices
0.155
0.778

Productivity
0.622
0.862

Money
0.368
0.422

Error correction
0.447
0.317

Panel B. Wages Measured by Unit Labor Cost
Are prices caused by
Time period
1960:IQ–1999:IIIQ
1980:IQ–1999:IIIQ

Unit labor costs
0.146
0.451

Money
0.215
0.687

Error correction
0.233
0.264

Are unit labor costs caused by
Time period
1960:IQ–1999:IIIQ
1980:IQ–1999:IIIQ

Price
0.000
0.000

Money
0.000
0.102

Error correction
0.000
0.150

Panel C. Wages Measured by Average Hourly Earnings with Productivity Included Separately
Are prices caused by
Time period
1960:IQ –1999:IIIQ
1980:IQ –1999:IIIQ

Wages
0.222
0.269

Productivity
0.330
0.217

Money
0.296
0.492

Error correction
0.012
0.185

Are wages caused by?
Time period
1960:IQ –1999:IIIQ
1980:IQ –1999:IIIQ

Prices
0.631
0.250

Productivity
0.403
0.143

Money
0.361
0.245

Error correction
0.000
0.002

a. Each column reports the p-values (that is, the level of statistical significance) for the test that the
column variable does not Granger-cause either wage inflation or price inflation.

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FEDERAL RESERVE BANK OF CLEVELAND

ductivity-adjusted wage growth appear to help forecast inflation.’’ Second, Clark (1998) has
recently explored whether producer prices help predict consumer prices on both an in-sam
ple (Granger-causality) and an out-of-sample basis. He finds that while there is evidence that
producer prices Granger-cause consumer prices, the nature of these relationships is so fragile that they simply cannot be used on an out-of-sample basis that would be useful for policy purposes. Finally, recent work by Hogan (1998) finds that even in a price-equation,
Phillips-curve specification, unit labor costs are not helpful for predicting U.S. inflation.6

Updated Granger-Causality Tests
Table 1 presents our own tests for Granger-causality between wages and prices, using the
most recent quarterly data available for the United States. 7 Panel A of the table reports evidence on which variables cause prices and wages. The regression includes lagged inflation,

6. Alternatively, Gali and Gertler
(1999) find that a measure of the
marginal cost of labor is a relevant
determinant of inflation. However,
their findings do not consider the
simultaneity or causality issues that
are the focus of our work.
7. All data were current as of
December 7, 1999.

lagged money growth, lagged wage growth, lagged productivity, the lagged relative growth
in energy and food prices, dummy variables for the Nixon wage and price controls, and a
constant (see the appendix for a complete description of the data).8 The lag length for each
variable is set to four. The regression also includes an error correction term that has been
estimated to find the long-run relationship between the log level of wages, prices, money,
and productivity.9 Each panel first reports the evidence on whether the column variable
Granger-causes prices, followed by the evidence for whether the column variable Grangercauses wages. The null hypothesis is that there is no Granger-causation, and each column
reports the level of statistical significance that one can reject this null hypothesis —the socalled “p-values.” In general, a p-value of less than 0.1 is evidence of Granger-causality.
The results in the top panel suggest that for the full sample, 1960:IQ to 1999:IIIQ, the
evidence does not show that wages Granger-cause inflation, but rather that prices are driven by their long-run relationship with other variables in the model, as identified by the error
correction term. Nor do price inflation and productivity growth Granger-cause wage inflation. Price and wage changes are best predicted by their own lags in these estimates.1 0 These
results should be viewed as reasonably consistent with those in the literature in that little or
no Granger-causality has been found for wages. The inability of these variables to forecast
one another “in-sample” continues for the more recent period, 1980:IQ to 1999:IIIQ.
Panel B of table 1 reanalyzes whether wages cause prices, except wages are now measured by unit labor costs. The findings suggest that for both samples, prices Granger-cause
unit labor costs, while unit labor costs do not Granger-cause prices. In addition, money
and the long-run error correction term Granger-cause prices in the full sample. These findings further reinforce the view that wages (even corrected for productivity growth) have

8. These Granger-causality tests do
not include a measure of the output gap, as recent evidence suggests that such measures are highly unreliable. Orphanides and van
Norden (1999) demonstrate that in
some cases the revision in the output gap is as large as the total
variability of the output gap. We
also do not report results for similar tests, including an import price
index, as recommended by Ghali
(1999), because including the
index restricted the period of
analysis but did not materially alter
the results from those reported for
1980:IQ to 1999:IIIQ.
9. As pointed out by Ghali (1999),
these cointegrating regressions do
not include a constant. They were
estimated using the dynamic OLS
method of Stock and Watson
(1993).
10. The weak performance of the
money measure might be associated in part with sharply larger rates
of money growth in 1999, which
were associated with Y2K liquidity
concerns. We examined the same
relationships excluding the last
year and found essentially equivalent results.

no independent causal effect on prices. As is found often in the literature, there is more
evidence that prices Granger-cause wages than that wages Granger-cause prices.
Panel C replaces compensation and the related unit-labor-cost series with a more conventionally measured wage. Compensation includes the value of benefits, bonuses, realized gains in option-based compensation, and the earnings of the self-employed. While it
5

POLICY DISCUSSION PAPERS

NUMBER 1, APRIL 2000

is important not to ignore other forms of compensation, these measures necessarily place
more weight on the occupations that may be removed from the production process.
Perhaps there is a stronger causal relationship between conventional wages and prices than
between compensation and wages as reported in panels A and B? The Granger-causality
results do not support this view. Only the long-run error correction terms are identified as
significant in panel C, when conventional wage data are used.

Lengthening the Forecast Horizon
Granger-causality tests are based on the one-period-ahead (in this case, one quarter) predictive capacity of a data series. Policymakers typically look further forward when setting
monetary policy. While a one-quarter-out estimate of inflation could be used to generate a
“dynamic” forecast by plugging in predictions of the explanatory variables to yield a longerhorizon forecast, this process adds no additional information. Instead, we use statistical
tests analogous to those yielding the table 1 results to test the information content of wage
changes and inflation at a longer horizon. Table 2 repeats the table 1 tests at the more
policy-relevant lead time of one year.
TABLE 2

ONE-YEAR FORWARD PREDICTION TESTSa
Panel A. Wages Measured by Compensation per Hour with Productivity Included Separately
Are prices predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Wages
0.298
0.029

Productivity
0.604
0.010

Money
0.006
0.000

Error correction
0.022
0.081

Are wages predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Prices
0.879
0.076

Productivity
0.340
0.557

Money
0.071
0.149

Error correction
0.257
0.926

Panel B. Wages Measured by Unit Labor Cost
Are prices predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Unit labor costs
0.176
0.009

Money
0.084
0.001

Error correction
0.814
0.002

Are unit labor costs predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Price
0.000
0.208

Money
0.460
0.042

Error correction
0.328
0.130

Panel C. Wages Measured by Average Hourly Earnings with Productivity Included Separately
Are prices predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Wages
0.822
0.174

Productivity
0.469
0.715

Money
0.008
0.231

Error correction
0.002
0.046

Are wages predicted by
Time period
1960:IQ – 1999:IIIQ
1980:IQ – 1999:IIIQ

Prices
0.611
0.189

Productivity
0.241
0.165

Money
0.054
0.706

Error correction
0.007
0.090

a. Each column reports the p-values (that is, the level of statistical significance) for the test that lags 5 to 8
of the column variables do not aid in predicting either wage inflation or price inflation.

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FEDERAL RESERVE BANK OF CLEVELAND

Throughout table 2, more variables offer a boost to the model’s predictive power at the
longer horizon. In panel A, wage changes still do not predict inflation in the full sample.
However, prices and wages help predict each other in the second half of the sample.
Focusing on the later period, all variables contribute to better estimates of inflation: wages,
productivity, money, and the error correction term. Interestingly, money performs far better
at one year into the future. Except for predicting wages since 1980, money is consistently
significant. Panel B shows similar results when unit labor costs are used as the wage measure. Only in the later period are unit labor costs a significant factor in inflation estimates,
but money and the error correction term are frequently significant. In panel C, wages never
matter and money works only over the full time span. Overall, there is some support for
looking at wages as an indicator of impending inflation, although there is just as much evidence that wages are caused by inflation movements. Money is surprisingly important in this
analysis; in fact, it is the most reliable indicator for both wage and price changes.

Judging the Predictive Performance
A shortcoming of Granger-causality tests is that they are based on in-sample estimates of the
data. However, financial markets attempt to use this information to predict future inflation,
so it is also important to analyze whether empirical models of inflation estimated over available data are useful for forecasting inflation for a later period (such as the 1990s). Financial
forecasts are likely to use all of the data available at a given date to predict inflation a num
ber of periods out. Results from such an experiment are presented in table 3a. A number of
inflation equations were forecasted over 1990:IQ to 1999:IIIQ using data from 1960:IQ to the
assumed forecasting date, which depends on how far out the forecasting exercise is being
conducted.1 1 The models are labeled in the first column. The univariate model contains a time
trend, the lagged price level (in logs), four lags of inflation, four lags of relative growth in
energy and food prices, dummy variables for the Nixon wage and price controls, and a constant (see the appendix for a complete description of the data). The model labeled money
also includes the lagged level of money (in logs) and four lags of money growth. The wage,
productivity, and unit-labor-cost models are similarly defined.1 2
The second column in table 3a reports the root mean squared errors (RMSE) of one-

11. These static forecasts examine how
an equation, estimated over one
time period, fits over a later sample
with quarterly reestimation of the
empirical relationships. Forecasts
beyond a quarter again are based
on a static forecasting procedure. A
dynamic forecast would involve
estimating an inflation equation
and then forecasting each subsequent period based on earlier forecasts. Static forecasts typically do a
much better job of predicting than
do dynamic forecasts. See also
Clark (1997).
12. In contrast to the in-sample
Granger-causality tests, where
model specification is essential for
making correct statistical inference
for hypothesis tests, we take a less
structural approach for our out-ofsample comparison of RMSE.

quarter-ahead forecasts. Forecasts with lower RMSE are better than those with higher RMSE.
The values in parentheses are the probability that the money, wage, productivity, unitlabor-cost, and hourly-earnings models of inflation have lower RMSE than does the simple
univariate model. The null hypothesis is that these models do not have lower RMSE than

13. The degrees of freedom are adjusted to account for the lack of independence in one-year-forward forecasts.

the univariate model. P-values less than 0.1 generally suggest that the models outforecast
the simple univariate inflation model. The third column repeats the analysis with a forecast
of inflation one year in advance.1 3
Table 3b performs a similar exercise, except that we now use these key variables to predict wage growth on an out-of-sample basis. The structure of the table is the same as table
3a. Two key results are revealed in these wage growth forecasts. First, neither inflation nor
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POLICY DISCUSSION PAPERS

NUMBER 1, APRIL 2000

productivity has been very helpful in forecasting wage growth out-of-sample. In fact, they
have only a marginal effect in lowering the RMSE; and in one instance, they actually raise
it. Second, money growth performs well in predicting wage growth at both horizons.
The out-of-sample forecasts confirm the absence of convincing evidence that wages are
good at forecasting prices on an out-of-sample basis. In particular, while the RMSE for the
wage model is lower than that of the univariate model at both horizons, the estimated standard error of the improvement in the forecast is so large that we cannot reject the null
hypothesis. 1 4 This outcome is not unusual. The two other wage measures and productivity
also offer some mean improvement in the forecast, but they are not consistent enough to
be statistically significant. Productivity stands out because it offers the lowest RMSE, but the

TABLE 3A

OUT-OF-SAMPLE FORECASTING OF PRICE INFLATION
FORECASTS OVER 1990:IQ–1999:IIIQ
One Quarter Ahead
Root Mean Squared Error
(p-value)

One Year Ahead
Root Mean Squared Error
(p-value)

0.821

1.240

Money

0.758
(0.311)

0.961
(0.046)

Wages

0.711
(0.236)

0.893
(0.335)

Productivity

0.698
(0.118)

0.808
(0.208)

Unit labor cost

0.768
(0.251)

1.090
(0.106)

Average hourly earnings

0.793
(0.673)

14. The comparison of forecast errors
is robust to heteroskedasticity and
serial correlation of unknown form
using a standard Newey-West correction. This approach is similar to
that in Blomberg and Hess (1996).
See Diebold and Mariano (1995)
for general comparisons of forecast
accuracy tests.

1.143
(0.677)

Model

Univariate

The univariate model contains four lags of inflation, four lags of the relative growth in energy and food
prices, dummy variables for the Nixon wage and price controls, the long-run error correction term, and a
constant (see the appendix for a complete description of the data). The model labeled money also
includes the lagged level of money (in logs) and four lags of money growth. The wage, productivity, and
unit-labor-cost models are similarly defined. The p-value reports the level of statistical significance for the
test of the null hypothesis that these models do not have lower RMSE than the univariate model.

TABLE 3B

OUT-OF-SAMPLE FORECASTING OF WAGE GROWTH
FORECASTS OVER 1990:IQ–1999:IIIQ
One Quarter Ahead
Root Mean Squared Error
(p-value)

One Year Ahead
Root Mean Squared Error
(p-value)

1.780

1.904

Money

1.528
(0.126)

1.602
(0.046)

Price inflation

1.747
(0.850)

1.765
(0.497)

Productivity

1.719
(0.717)

1.940
(NA)

Model
Univariate

The univariate model contains four lags of wage growth, four lags of the relative growth in energy and food
prices, dummy variables for the Nixon wage and price controls, the long-run error correction term, and a
constant (see the appendix for a complete description of the data). The model labeled money also includes
the lagged level of money (in logs) and four lags of money growth. The inflation and productivity models
are similarly defined. The p-value reports the level of statistical significance for the test of the null hypothesis that these models do not have lower RMSE than the univariate model.

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FEDERAL RESERVE BANK OF CLEVELAND

forecast improvement is unfortunately quite variable. Importantly, however, money does
contain essential information for forecasting prices over a one-year horizon during this time
period. Money also offered reliable improvements in the forecast in the 1990s, suggesting
that those who wish to gauge where future inflation is headed should consider moneysupply developments.

Conclusion
There is little systematic evidence that wages (either conventionally measured by compensation or adjusted through productivity and converted to unit labor costs) are helpful for
predicting inflation. In fact, there is more evidence that inflation helps predict wages. The
current emphasis on using changes in wage rates to forecast short-term inflation pressure
would therefore appear to be unwarranted. The policy conclusion to be drawn is that inflation can appear regardless of recent wage trends.

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FEDERAL RESERVE BANK OF CLEVELAND

Data Appendix
Average hourly earnings: Average hourly earnings of production workers. The data are seasonally adjusted, monthly, available from January 1964 to September 1999. Quarterly average
used in analysis.
Money: The adjusted St. Louis Monetary Base. The data are available from 1950:IIQ to
1999:IIIQ. Obtained from FRED, Federal Reserve Bank of St. Louis.
Prices: The personal consumption expenditure deflator. The data are seasonally adjusted,
quarterly, available from 1947:IQ to 1999:IIIQ, 1992 = 100.
Productivity: Nonfarm business. The data are seasonally adjusted, quarterly, available from
1947:IQ to 1999:IIIQ, 1992 = 100.
Relative inflation in food and energy prices: CPI inflation less the growth rate of the CPI
excluding food and energy.
Unit labor costs: Nonfarm business, unit labor costs. The data are seasonally adjusted,
quarterly, available from 1947:IQ to 1999:IIIQ, 1992 = 100.
Wage and price control dummy variables: The first dummy variable takes the value 1 durring 1971:IIIQ to 1972:IVQ and 0 otherwise. The second dummy variable takes the value
1 during 1973:IQ to 1974:IVQ.
Wages: Nonfarm business, total compensation. The data are seasonally adjusted, quarterly,
available from 1947:IQ to 1999:IIIQ, 1992 = 100.
The data were transformed by taking logs, and quarterly growth rates are annualized.

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FEDERAL RESERVE BANK OF CLEVELAND

References
Blomberg, S. Brock, and Gregory D. Hess. “Politics and Exchange Rate Forecasts,” Journal
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13

POLICY DISCUSSION PAPERS

NUMBER 1, APRIL 2000

Rissman, Ellen R. “Sectoral Wage Growth and Inflation,” Federal Reserve Bank of Chicago,
Economic Review, July/August 1995, pp. 16–28.
Staiger, Douglas, James H. Stock, and Mark W. Watson. “The NAIRU, Unemployment, and
Monetary Policy,” Journal of Economic Perspectives, vol. 11, no. 1 (Winter 1997), pp. 33–50.
Stock, James H., and Mark W. Watson. “Forecasting Inflation,” Journal of Monetary
Economics, vol. 44, no. 2 (October 1999), pp. 293– 335.
, and

. “A Simple Estimator of Cointegrating Vectors in Higher Order

Integrated Systems,” Econometrica, vol. 61, no. 4 ( July 1993), pp. 783 – 820.

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