Yajun Peng and Edward Renshaw,’ State University of New York at Albany

The literature on monetary policy and interest rate reaction functions is rather voluminous [Barth, Sickles, and Wiest 1982, Table 5. p. 64, and the references at the end of McNees, 1986). Business forecasters, on the other hand, have not had very good luck at predicting the direction of short-term interest rates in recent years. The consensus forecasts compiled by the Blue Chip Economic Indicators Survey in early January for the average yearly offering yield on new issues of 91 -day Treasury bills (T-bill) has only outperformed a no-change prediction based on the last offering yield for the preceding December on three occasions since 198 1 (see Table I )

Part of the blame for poor financial predictions may be a tendency on the part of model builders to focus their attention on the evils of inflation and unemployment that the Federal Reserve is known to be concerned about (Renshaw and Trahan. 1990). rather than the “winds of change“ that are use4 !o direc: z~~~tary p&j. X&ee~ ( 1986) has shown that forecasts-of what might happen to inflation and unemployment are quite helpful in explaining quarterly changes in short-term interest rates.

Table 2 contains some regression coefficients that endeavor to explain the average following-year change in new offering yields on 91 -day Treasury bills relative to the !ast offering yield in December for the years 1948-90. The variable with the most forecasting ability that we have discovered is the Dec.-Dec. percentage change in the Commerce Department’s composite index of 11 leading economic indicators. it should be noted that the relationship between these two variables is not exactly linear.

A slightly higher adjusted R-square and t- statistic can be obtained by correlating the change in the T-bill yield with a dummy variable equal to 1 .O if the percentage change in the index of leading economic indicators was over 6.1, - 1 .O if the change was less than 2.0, and zero otherwise. For every variable we have discovered in this exercise (or efforts to improve on the results published in The Pruc~ticul Forecuskv’s Alrnanuc)’ that appears to have some statistically significant ability to forecast interest

‘The x!t.t~s are grateful to the New York Lottery for financial support.

Address c.orrt~.sportrIent.e to Edwurd Renshu~*, Dtp~rtmwt of Econorrtic*s. StuttJ Uniwrsrt.v of‘

NW York ut Alhtqv. Alhy. NY 12222.

Received March 1993; final draft accepted May 1993.

‘Renshaw ( 1992). This almanac emphasizes consensus forecasts. which have the property of

234 Y. Peng and E. Renshaw

--

Table 1: Are Changes in Short-Term Interest Rates Predictable? Evaluating the Consensus Forecasts of the Average Yield on New Three-Month Treasury Bills by the Blue Chip Economic Indicator’s Survey

Year

Blue Chip Consensus Forecast (January Survey)’

(1)

Last Offering Yield for

the Preceding Decemberb

(2)

Actual Average T-BiPI

Offering Yield

(3)

1982T 10.8( -0. l)Bd

1983 7.7(0.9)

1984 8.8(0.8)

1985 8.6( - 1.1)

1986 7.1( - 1.1)

1987 5.4(0.4)

1986 6.0(0.7)Bd

1989 7.8(0.3j

1990 7.W.4)

1991T 6.3( - 0.9)Bd

1992 4.2

Mean Absolute Error

11.7( - 1 .O) 10.7

8.0(0.6) 8.6

i(.9(0.7) 9.6

7.8( -. 0.3) 7.5

7.0( - 1 .O) 6.0

5.7(0-l) 5.8

5.7(1 .O) 6.7

8.2( -0.1) 8.1

7.8( -0.3) 7.5

6.8( - 1.3) 5.4

4.1 3

1982-91 0.67 0.65

“The January survey is conducted very early in the month. The figures in parentheses are the forecasting errors obtained by subtracting the Blue Chip consensus forecast from the actual average yield on new offerings in column 3.

“The average last offering yield on 91-day Treasury bills for the preceding month of December published in Moody’s Municipal & Governmcnr Manual. The figures in parentheses are error terms obtained by subtracting the last offering yield from the actual average new offering yield in column 3.

‘T identifies years containing a recessionary trough in business activity. dB identifies years when the forecasting error for the Blue Chip consensus was smaller ab-

solutely than the no-change forecasting error in column 2.

rates (or financial returns), one can obtain at least as good, and in most cases better, predictions with a dummy variable, to identify nonlinear threshold effects, instead of the not uncommon assumption of a linear relationship.

The importance of nonlinear threshold effects is even more apparent for relative food inflation, the other wind of change variable in Table 2. If the Dec.-Dec. per- centage change difference between the food component of the consumer price index (CPI) and the all- item CPI is replaced with a dummy variable equal to 1 .O, when

always being equal to or better than the majority of forecasts, and also !ooks for nonlinear thresholds when using statistical indicators to predict returns in the stock and bond markets.

MODELING THE FEDERAL RESERVE’S POLICY 235

Table 2: Sope Regression Coefficients That Help to Explain the Difference between the iast Offering Yield for 91 -Day Treasury bills in December and the

Average Following-Year Yield for New 91 -Day Treasury Bills, 1948-90

Regression Coefficients”

Independent Variables (1) (21 (3) (4) --

Constant term

Dec.-Dec. percentage change in :he composite index of leading economic indicators

Dummy variable equal to 1 .O if the percentage change in the index of leadmg economic indicators was over 6. I percent, - 1 .O if the change was less than 2.0 percent, and zero otherwise

Dec.-Dec. percentage change in the food component of the CPI minus the Dec.-Dec. percentage change in the all-item CPI

Dummy variable equa! to I .O if the above measure of food inflation was equal to I .2 percentage points or more and zero otherwise

-0.261 - 0.245 - 0.224 ( - 2.483) ( - 2.387) ( - 2.382)

0.056 0.054 (4.383) (4.391)

0.47 1 0.434 (4.457) (4.573)

0.067 ( I .856)

0.757 (3.444)

Adjusted R-squared S.E. of Regression Durbin-Watson Statistic

Summary Statistics 0.302 0.315 0.342 0.455 0.630 0.625 0.612 0.557 2.083 2.143 2.131 2.293

“The parentheses contain r-statistics for the hypothesis that the coefficients true value is zero

this difference is equal to 1.2 percentage points or more, and zero otherwise, the adjusted R-square increases from 0.048 to 0.191, and the t-statistic associated with the regression coefficient from 1.77 to 3.30.

Since the end of World War II the Federal Reserve has always let short-term interest rates rise in response to a notable food shortage in an effort to prevent a spillover of inflation to other sectors of the U.S. economy. Inflation has turned out to be such a stubborn problem in the post-1948 period, however, that the Fed has not been very worried about the deflating effect of a decline in food prices. unless the leading economic indicators were signaling a possible recession.

There are only six interest rate directional errors for co!umn 4 in Table 2 over the 43 in sample forecast years from 1949 to 1991. Half of these directional errors (the errors for 1957, 1969, and 1980) occurred during years containing an official peak in business activity (as defkd by the Nationa! R uurcau of Economic Research) and are

characterized by an average increase in the T- bill offering yicfd instead of the dcckc

236 Y. Peng and E. Renshaw

in interest rates predicted by our wind of change model. The Fed, it would stem, has sometimes been slow to react to the possibility of an economic recession.

The standard errors for the forecasting equations in Table 2 are rather sizable. This is understandable considering the propensity of the leading economic indicators to predict more recessions than have actually occurred and the belief on the part of many economists that the impact of changes in monetary policy on inflation and economic activity is subject to rather long a;rd variable lead times. The Fed, moreover, appecrs to have experimented with widely varying interec,? rate policies during some of the business expansions and contractions that have occur-ted in the post- 1948 period (McNees, 1992; Renshaw. 1992, Table 1.88).

The real test of a forecasting rncrdel is how well it performs in an out-of-sample period. As of the beginning of 1992, the variables in column 4 of Table 2 were predicting an average decline in the Treasury bill rzte of 0,224 percentage points for 1992. While the actual decline will probably-be greater, the winds of change approach does look good in comparison to the increase in short-term interest rates that was predicted by the majority of forecasters surveyed by the Blue Chip Economic Indicators

Newsletter in January 1992.

REFERENCES

Barth, James, Sickles. Robin, and Wiest, Philip (1982) Assessing the Impact of Varying Eco- nomic Conditions on Fcxteral Reserve Behavior. Journal of Macroeconomics 4( 1) 47-

70.

McNees, Stephen (1986) Modeling the Fed: /ii Forward-Looking Monetary Policy Reaction

Function. Neuv England Economic Review November 3-25.

McNees. Stephen ( 1992) The 1990-91 Recession in Historical Perspective. Neuv En&znd Eco-

rzomic Review January 3-22.

Renshaw, Edward, and Trahan, Emery ( 1990) Presidential Elections and the Federal Reserve’s

Interest Rate Reaction Function. hnrnal of Policy Modeling I2( I ) 29-34.

Renshaw, Edward, Ed. (1992) 771~ Prucricul Forecaster’s Almanac.Homewood. IL. Business

One Irwin.