The Federal Reserve's reaction function under Greenspan: An ordinal probit analysis

PETER G. VANDERHART Bowling Green State University

Bowling Green, Ohio

The Federal Reserve's Reaction Function under Greenspan: An Ordinal Probit Analysis*

An ordinal probit technique is employed on monthly data to explain discrete changes in the targeted Federal funds rate since August 1987. Changes in industrial production and precursors of final good inflation appear to have significant effects on the Federal Reserve's behavior, while consumer prices, unemployment, and other variables often thought to influence its actions do not. These results are robust to a variety of alternative specifications, including the use of greenbook forecasts and those derived from Taylor's Rule. The results suggest that the Federal Reserve seeks to stabilize output and preemptively control inflation before it reaches the consumer level.

This paper is an analysis of how the Federal Reserve has responded to changing economic conditions in recent years. Much published work in this area has been concerned with Federal Reserve behavior in previous historical periods. Moreover, most previous work has modeled the magnitude of the Federal Reserve's actions as continuous, while it is generally perceived that the Federal Reserve's recent actions have involved discrete changes in the targeted Federal funds rate. This paper seeks to update these "monetary reaction functions" and to model their discrete nature. ]

The paper is organized as follows: An explanation of the ordinal probit model along with a rationale for its use appears in the following section. A second section describes the data used in this analysis. The results of several specifications are presented in a third section. A final section provides a discuss ion .

*I would like to thank Owen Humpage, Jennifer Ransom, and Kevin Sargent of the Federal Reserve Bank of Cleveland for their help in obtaining some of the data. This work has also benefitted from comments by Tim Fuerst, Joseph Haimowitz and anonymous referees.

lit is important to note that this paper is an analysis of Federal Reserve actions rather than an analysis of the stance of monetary policy: Changes in the Federal funds target rate may merely be designed to maintain a given degree of tightness as economic conditions change; and not changing the target rate in the face of changing conditions may constitute a change in the tightness of monetary policy. Rather than seeking to describe how the position of monetary policy changes over time, this paper concentrates on the directly measurable behavior of the Federal Reserve, which is interesting in its own right.

Journal of Macroeconomics, Fall 2000, Vol. 22, No. 4, pp. 6314)00 Copyright © ")000 by Louisiana State University Press 0164-0704/2000/$1.50

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1. Ordinal Probit Analysis Rationale

Most past analyses model the Federal Reserve's decisions as consisting of marginal changes in a policy variable in every period. (See Barth, Sickles, and Wiest 1982; or Khoury 1990 for a review of previous studies.) While this is no doubt possible, in recent years the Federal Reserve has instead acted to change the targeted Federal funds rate relatively infrequently, and by a discrete amount when a change does occur (Meulendyke 1998). Fur- thermore, in recent years the Federal Reserve has kept the actual Federal funds rate very close to its target rate (Cleveland Federal Reserve 1998), suggesting that deviations of the actual rate from the target rate represent temporary noise and do not convey information about policy. This suggests that one should use the discrete and infrequent changes in the Federal funds target rate as the dependent variable in a reaction function covering recent Federal Reserve behavior. In such a setting an ordinary least squares analysis is inappropriate: Changes in the target rate are not distributed normally, which violates an assumption of the classical regression model. 2

An attractive alternative is to use limited-dependent variable techniques to analyze the infrequent and "lumpy" changes that are made. Econo- metric models of this type are not without precedent in the monetary reaction function literature: Potts and Luckett (1978) use discriminant function analysis to explain whether Federal Reserve policy from 1956-1975 was "tight" or "easy." Hakes (1990) uses a similar dependent variable in his linear probability and probit analyses. Eichengreen, Watson and Grossman (1985) and Davutyan and Parke (1995) have used probit analyses to model the operations of the Bank of England prior to World War I. Also, articles by Tootell (1996) and Chappell, Havrilesky and McGregor (1993) use the tri- nomial logit and ordinal probit models respectively to explain individual FOMC member's voting behavior. Although they do not involve the use of limited-dependent variable techniques, other discrete measures of monetary actions can be found in articles using a narrative approach to explain the effects of monetary policy (see for example Romer and Romer 1989; and Boschen and Mills 1995).

Model A natural way to think of the Federal Reserve's discrete changes in

the Federal funds rate target is as ordered from one extreme to the other,

~See Judge et al. (1985). I f the dependent variable is not distributed normally, neither will be the residual. Residuals from an OLS regression on the data described here contained more values very close to 0 and at the extreme tails, and fewer values in between than would be expected if the residuals were distributed normally. This is consistent with what one would expect given the infrequency of changes in the target rate, and the relatively large value when a change does occur.

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The Federal Reserve's Reaction Function under Greenspan

One limited-dependent technique that incorporates an ordered response is the ordinal probit. In this model the underlying propensity to make a given change in the target rate is unobserved, but the observable category of the response in time t is denoted by Yr. The probability that the observation for time t falls into the jth category can be expressed as:

e r ( L = j ) = ~ ( o 9 - f~'x~) - ¢ ( o 9 _ 1 - f ~ ' x , ) , (1)

where the (I)s are cumulative standard normal functions, the as are threshold values, the xs are explanatory variables, and the ~s are the coefficients of interest. The threshold values can be thought of as the boundaries of each category's share of the response variable's cumulative distribution function (See Greene 1993). The highest and lowest a terms cannot be identified, so in practice they are set to very large positive and negative numbers respectively. With these probabilities defined, the log likelihood function can be written as

c

log(L) = ~ "~ log[~(o 9 - ~'xt) - ~(o9_1 - I]'x~)], j = l Yt=j

(9,)

where C is the number of categories. There is no closed-form solution for the [3 vector that maximizes this expression, but it can be estimated using iterative methods. In this model each element of [3 is not interpreted as an explanatory variable's effect on a dependent variable, but rather as an indi- cation of the variable's effect on the probability that a given response occurs. See Maddala (1983) for a more thorough exposition.

2. Data A natural choice for the beginning of a period covering recent Federal

Reserve behavior is the beginning of Alan Greenspan's chairmanship, August of 1987. Limiting the period in this way avoids the criticism that the reaction function could change as the chairmanship changes (Hakes 1990). 3 The end of the period examined here is July of 1999.

The dependent variable is defined using changes in the Federal funds target rate from the beginning of one month to another. Although one might be tempted to specify a six-week interval to correspond to the FOMC meet-

~Of course it does not avoid the crilfeism that the behavior of the chair could change, or that changes in the composition and/or behavior of the Federal Reserve Open Market Committee (FOMC) could occur over the period examined.

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Peter G. VanderHart

TABLE 1. Descriptive Statistics

Std. Variable Mean Dev. Ma~mum Minimum

Change in Unemployment Rate -0.0147 0.1419 0.3 -0.4 Percentage Change in Industrial Prod 0 .2564 0.4934 1.4470 - 1.3284 Percentage Change in CPI 0.2666 0.1647 0.9494 0.0 Percentage Change in Intermediate PPI 0.1309 0.4224 1.5789 - 1.0354 Percentage Change in Spot Prices -0.0563 1.6722 3.6544 -5.1419 Percentage Change in AHE 0.2711 0.1313 0.6696 -0.1094 Percentage Change in M2 0.3385 0.2623 1 .0314 -0.2934 Percentage Change in Exchange Rate 0.4998 1.2392 4 . 0 4 7 1 -2.8482 Percentage Change in S&P 500 1.0743 3.3122 11.2968 - 12.5464 Percentage Change in Housing Starts 0.2706 6.9829 23.9808 - 17.6471 Percentage Change in Retail Sales 0.4602 0.8058 3.0645 - 1.6288 Forecast Change in Unemployment - 0.0182 0.1402 0.3 - 0.5 Forecast % Change in Industrial Prod 0.1649 0.3098 0.6279 - 0.8010 Forecast % Change in CPI 0.1391 0.0639 0.2009 0.0793

ing interval, an examination of the target rate data over the period considered reveals that fewer than half of the target rate changes have occurred within a few days of an FOMC meeting (Federal Reserve Bank of New York 1999; Federal Reserve Bank of Minneapolis 1999).

In the initial analysis the Federal Reserve's actions in any given month are classified into one of five categories: Large decrease (a decrease in the Federal funds target rate of more than 0.25), small decrease (a deerease in the target rate of 0.25 or less), no change, small increase (an increase in the target rate of 0.25 or less), and large increase (an increase in the target rate of more than 0.25). Thus the Federal Reserve's responses are ordered from easier to tighter monetary actions. Over the time period examined there are 7 large decreases, 22 small decreases, 95 non-changes, 8 small increases, and i1 large increases.

The explanatory variables are also defined using monthly intervals. Be- cause these statistics are reported with a lag, care is taken to lag the explanatory variables to reflect their availability. For instance, PPI numbers are reported in the middle of the month following the month to which they refer (Frumkin 1990). Thus the January PPI figures are released in the middle of February and thus are assumed to influence the February-March change in the Federal funds target rate. Changes in variables that can be viewed con- temporaneously are not lagged. The descriptive statistics appear in Table 1. 4

4Most of this data is available at the St. Louis Federal Reserve's web site (hap://

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3. Results Initial Specifications

The first specification tests the hypotheses that the Federal Reserve is more likely to change its target rate for the Federal funds rate in response to changes in variables that reflect the most commonly cited goals of monetary policy: Low unemployment, a high level of economic growth, and low inflation (McNees 1986; Federal Reserve Board 1994; Judd and Rudebusch 1998). Specifically, the change in the unemployment rate, the percentage change in industrial production, and the percentage change in the CPI are selected as explanatory variables. The results appear in the first column of Table 2.

The values for the thresholds, while apparently merely constants, do contain information of interest. The difference between the second and third thresholds is relatively large, suggesting that the Federal Reserve generally is reluctant to make changes in the Federal funds target rate. In other words, it takes fairly extreme values of the explanatory variables to push the Federal Reserve over one of these thresholds. The difference between the third and fourth thresholds is relatively small, suggesting that once the Federal Re- serve decides to increase the target rate slightly, it does not take much more to precipitate a more substantial move.

The coefficient on change in the unemployment rate does not enter the regression with statistical significance, casting doubt on the contention that the Federal Reserve moves the Federal funds target rate in response to changes in joblessness. However the variable capturing the change in industrial production has a coefficient that is positive and statistically significant, suggesting that strong economic growth causes the Federal Reserve to be more likely to increase the target rate and less likely [o decrease it. The coefficient for the percentage change in CPI is not statistically significant at the 5% level, indicating somewhat curiously that the Federal Reserve does not respond to inflation at the consumer level. Using core CPI numbers instead did not substantially improve the performance of this factor.

One explanation for the performance of the CPI variable is that the Federal Reserve may pay attention to statistics that are precursors to inflation, rather than to inflation at the final goods level. To test this hypothesis the CPI variable is replaced with three variables measuring the percentage change in the PPI for intermediate goods, the percentage change in the CRB spot market index of raw material prices, and the percentage change in average hourly earnings. The results appear in the second column of Table 2. The coefficients for PPI and spot prices enter with the expected positive

www.stlsfrb.org/fred/). Some data is not available in machine-readable format, so a complete set of the data is available from the author on request.

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Peter G. VanderHart

TABLE 2. Ordinal Probit Results

(i) (2) (3)

a 1 - 1.564" - 1.469" - 1.086" ( - 5.259) ( - 5.300) ( - 3.020)

c~ 2 - 0.609* - 0.461 0.011 ( - 2.561) ( - 1.895) (0.033)

% 1.510" 1.949" 2.639* (5.516) (6.509) (5.984)

a 4 1.843" 2.359* 3.083* (6.148) (6.467) (6.460)

Change in Unemployment Rate - 0.774 - 0.242 - 0.471 ( - 0.862) ( - 0.255) ( - 0.432)

Percentage Change in Industrial 0.861" 0.819" 0.861" Production (3.799) (3.340) (3.339) Percentage Change in CPI 0.240

(0.374) Percentage Change in Intermediate PPI 0.559* 0.718"

(2.116) (2.344) Percentage Change in Spot Market 0.261" 0.307* Prices for Raw Materials (4.085) (3.653) Percentage Change in Average Hourly 1.120 1.720" Earnings (1.436) (2.009) Percentage Change in M2 0.265

(0.526) Percent Change in Trade-Weighted 0.162 Exchange Rate (1.479) Percentage Change in S&P 500 0.030

(0.938) Percentage Change in Housing Starts 0.012

(0.692) Percentage Change in Retail Sales 0.334*

(2.158) - 2*loglikelihood 283.012 257.740 241.773

*significant at the 5% level t-statistics appear in parentheses

sign and are statistically significant. Adding the CPI variable to this specification genera ted a statistically insignificant coefficient, and did not substantially change the results for the other variables_ Thus the results general ly suppor t the content ion that the Fede ra l Reserve reacts to precursors of inflation ra ther than to CPI inflation itself.

One might criticize these results for not including some of the economic factors that the Federa l Reserve may react to. An examination of

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FOMC minutes (Federal Reserve Board 1999) reveals that the information reviewed during each meeting nearly always includes a discussion of monetary aggregates, changes in foreign currency markets, the behavior of asset prices, and changes in interest rate sensitive types of spending. To test the hypothesis that these factors have a significant effect on the likelihood that the Federal Reserve changes the target rate, five additional variables are selected for inclusion, based on their availability at a monthly frequency': The percentage changes in M2, the trade-weighted exchange rate (foreign currencies per U.S. dollar), the S&P 500 stock market index, housing starts, and retail sales. The results appear in the third column of Table 2. Only one of the coefficients for these new variables is statistically significant (retail sales). The coefficients on industrial production, PPI, and spot prices maintain their strength, while the coefficient on average hourly earnings becomes statistically significant. Furthermore, the magnitude of the coefficients (when used to compute changes in the predicted probability of each action) indicate that industrial production and the inflation precursors are the most economically significant variables as well. These results suggest that few factors beyond changes in output and inflation precursors matter much to the Federal Reserve when it sets the target for the Federal funds rate. Thus in the work that appears below, we return to the specification appearing in the second column of Table 2.

Alternative Specifications One potential criticism of the specification above is that all target rate

changes greater than 0.25 (and less than - 0.25) are lumped into categories that include all extreme movements. Because some of these changes are more extreme than others, it may make sense to classify them in a category by themselves. To accomplish this, changes in the target rate of more than 0.5 (and less than -0.5) are separated into their own new categories. Two of the seven "large decrease" observations are reclassified into the most extreme decrease category, and three of the eleven "large increase" observations are reclassified into the most extreme increase category.

Results analogous to those in the second column of Table 2 appear in the first column of Table A1 in the appendix. The previous pattern in the thresholds is seen again here, and there is very little change in any of the coefficients. Little change is observed in the results analogous to those in

5Although fixed investment is also an interest rate sensitive type of spending frequently mentioned in the FOMC minutes, it is a quarterly statistic, and is thus unsuitable for the monthly frequency used here. Other statistics sometimes mentioned in the minutes (new unemployment claims, change in inventories, trade deficit, M3, capacity utilization) either did not enter significantly or entered very similarly to factors already included, and they are not included here to avoid cohnearity.

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Peter G. VanderHart

the first and third column of Table 2, and for brevity they are not presented here. This seven category specification proved to cause convergence prob- lems when the iterative program was applied to some of the alternative specifications below. Given the closeness of these results to the original five category specification, we return to the five category classification in what appears below.

For a number of reasons, one might object to including some of the earliest observations in the analysis: There was actually no target for the Federal funds rate for several days after the stock market crash of October 1987; the late 80s are sometimes described as a period in which the FOMC gradually returned to a close targeting of the funds rate (Meulendyke 1998); and perhaps most importantly, several target rate changes in the late 80s were not even multiples of 0.25. Eliminating the first 25 observations effec- tively eliminates these concerns and still covers a substantial portion of the Greenspan chairmanship. The results with this restricted sample appear in the second column of Table A1. The results are for the most part unchanged, except that the coefficient on PPI is no longer statistically significant. Results with specifications along the lines of the first and third columns of Table 2 are quite similar and are not reported here.

Past actions may play some role in the decision to change the target rate (McNees 1992). To capture this, four dummy variables are added, each indicating whether or not one of the four changes occurred the month before. The results appear in the third column of Table A1. The coefficients on lagged large actions are relatively small and statistically insignificant. However, the coefficients on lagged small changes do enter significantly. The negative sign attached to the lagged small cut variable suggests that a small cut in the target rate makes a subsequent cut more likely and a subsequent increase less likely. Analogously, the positive sign of the lagged small increase variable suggests that a small hike makes a subsequent increase more likely and a decrease less likely. As a whole, these results suggest that the Federal Reserve is likely to follow up small changes with further changes in the same direction, but that large changes do not precipitate future changes of any kind.

Several authors have pointed to the Federal Reserve's emphasis of forecasts of macroeconomic variables, and have incorporated them into their reaction function analyses (McNees 1986, 1992; Tootell 1996; and Froyen, Havrilesky and Wand 1997). Because the Federal Reserve releases its greenbook forecasts with a five-year delay, it is only possible to examine the period from the beginning of Greenspan's term to December of 1993. Further- more, the greenbook does not include forecasts of the inflation precursors or the other factors consider here, so we return to a specification that includes forecasts for change in the unemployment rate, percentage change

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in industrial production, and percentage change in the CPI. The first column of Table A2 contains estimates using forecasts for the current quarter, and the second column employs forecasts for one quarter ahead. 6 The results are quite consistent with column 1 of Table 2: In each column the forecasted change in unemployment and percentage change in CPI do not enter significantly, and forecasted percentage change in industrial production does. Using four quarter ahead forecasts yielded similar results (not presented here) for the CPI variable, while surprisingly the coefficients for production and unemployment turned negative and became statistically significant.

Much of the recent work in the reaction function literature has cen- tered around Taylor's Rule (Taylor 1993), which dictates that the Federal funds rate should be a function of the deviation of inflation from some target level and the deviation of output from its potential level. Several authors have found that the rule does well in explaining recent movements in the Federal funds rate (Judd and Rudebusch 1998; Taylor 1998). The results in this paper lend some support to this finding, as industrial production and some precursors of consumer inflation (although not CPI itself) are found to be important explanatory variables. However these results are not strict tests of the rule's use as an explainer of Federal Reserve actions, because the differenced form of Taylor's Rule implies that changes in the Federal funds rate should be a function of changes in the inflation rate (rather than the rate itself), changes in the percentage gap between potential and actual output (rather than percentage change in output), and no other factors.

To provide a specification closer to the dictates of Taylor's Rule, the CPI and industrial production variables are reformulated as described above7 and all other variables are omitted. The results are displayed in the third column of Table A2. Once again, industrial production is found to enter in a statistically significant way, while CPI does not. This differs from most empirical estimates of Taylor's Rule, which find that inflation is a significant explanatory variable.

There are a number of reasonable explanations for this difference. Perhaps the simplest is that this analysis uses variables different than those in most tests of Taylor's Rule to measure both output (industrial output rather than GDP), and inflation (the CPI rather than the GDP deflator). Obviously the use of the ordinal probit rather than OLS, and the lagging of data to reflect availability rather than the use of concurrent values, are also

6The greenbook forecasts display annualized rates of change for both industrial production and the consumer price index. Each of these are adjusted as necessary to be comparable to the monthly rates used in the previous results.

7potential output is estimated using a simple log-lineax trend for 1973 to present. The results displayed here axe almost unchanged when a shorter period (1987 to present) is used, or when a quadratic trend (see Clarida, Gall and Gertler 1998) is used.

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Peter G. VanderHart

candidates. Differences in the frequency of the data may also be to blame: The quarterly variable measuring final goods prices used in most other tests of Taylor's Rule may enter significantly because it captures the effect of early-quarter movements in inflation precursors on final prices. The monthly variables used in this paper may be detecting that the Federal Reserve re- sponds to movement in inflation precursors, while the analyses using quarterly data detect this only indirectly via the precursors' effect on CPI. Alter- natively, quarterly final goods price data may simply be less noisy than monthly data, allowing the statistical significance to shine through.

4. Discussion The results of these ordinal probit analyses suggest that under Green-

span the Federal Reserve changes its target for the Federal funds rate in response to changes in output and some precursors of consumer inflation. The analysis also suggests that the Federal Reserve does not react to changes in unemployment, the CPI, and a variety of other statistics frequently mentioned in the FOMC minutes. Furthermore, the results suggest that substantial movements in the explanatory variables are necessary to cause a small change in the target rate, and that once the threshold for a small increase is passed, it takes very little additional movement to trigger a large increase in the target rate. These results are robust to a number of alternative specifications, including the inclusion of lagged actions, the use of forecasts, and a reformulation along the lines of Taylor's Rule.

These results are consistent with some of the results from previous literature in the field but are somewhat at odds with other results. Specifi- cally, they are in line with most other recent work in that variations in output appears to be an important determinant of Federal Reserve behavior and that other non-price index factors do not. However past analyses have found that inflation at the final goods level is a significant determinant, while the present analysis disagrees and finds that price increases in intermediate goods, raw materials, and labor may instead influence Federal Reserve actions. While this difference may be due to this paper's use of a limited dependent variable model, it may also be due to a difference in the time period examined or the specification of explanatory variables.

One might be tempted to use the results of these ordinal probit analyses to attribute specific policy goals to the Federal Reserve. The results are certainly consistent with a Federal Reserve that is primarily concerned with fighting inflation by heading it off at the producer and raw material level, and with keeping output near its potential level. The problem with using monetary reaction functions to infer policy goals is that they combine both the motives of the Federal Reserve with its expectations of how the variables influence one another. As McNees (1986) puts it, "It is not possible to extract

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from a reduced form relationship.., the importance they attach to various social objectives." Only a more involved estimation procedure such as the dynamic programming estimation models described by Eckstein and Wolpin (1989) or the inverse-control theory methods of Salemi (1995) could hope to separate the Federal Reserve's motives from their expectations. Thus while one can be relatively sure that the Federal Reserve reacts to changes in production and inflation in the pipeline, it is not clear why they do so.

Received: January 1999 Final version: January 2000

References Barth, James, Robin Sickles, and Philip Wiest. "Assessing the Impact of

Varying Economic Conditions on Federal Reserve Behavior." Journal of Macroeconomics 4 (Winter 1982): 47-70.

Boschen, John F., and Leonard O. Mills. "The Relation between Narrative and Money Market Indicators of Monetary Policy." Economic Inquiry 33 (January 1995): 24-44.

Chappell, Henry W., Jr., Thomas M. Havrilesky, and Rob Roy McGregor. "Partisan Monetary Policies: Presidential Influence through the Power of Appointment." Quarterly Journal of Economics (February 1993): 185-218.

Clarida, Richard, Jordi Gall, and Mark Gertler. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory." NBER Working Paper 6442, March 1998.

Cleveland Federal Reserve. Economic Trends. October 1998. Davutyan, Nurhan, and William R. Parke. "The Operations of the Bank of

England, 1890-1908: A Dynamic Probit Approach." Journal of Money, Credit, and Banking 27 (November 1995): 1099-1112.

Eckstein, Z., and K. Wolpin. "The Specification and Estimation of Dynamic Discrete Choice Models." Journal of Human Resources 24 (1989): 562-98.

Eichengreen, Barry, Mark W. Watson, and Richard S. Grossman. "Bank Rate Policy under the Interwar Gold Standard: A Dynamic Probit Model." Economic Journal 95 (September 1985): 725-45.

Federal Reserve Bank of Minneapolis. "Historic FOMC Meeting Dates." http://woodrow.mpls.frb.fed.us/info/policy/dates-hist.html, 1999.

Federal Reserve Bank of New York. "'Historic Changes of the Federal Funds Rate and the Discount Rate." http://www.ny.frb.org/pihome/statistics/ dlyrates/fedrate.html, 1999.

Federal Reserve Board. "Monetary Policy and the Economy.'" In The Federal Reserve System: Purposes and Functions. 8th ed. Washington, D.C., 1994.

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Froyen, Richard T., Thomas Havrilesk-y, and Roger N. Waud. "'The Asym- metric Effects of Political Pressures on U.S. Monetary Policy." Journal of Macroeconomics 19 (Summer 1997): 471-93.

Frumkin, Norman. Guide to Economic Indicators. Armonk, New York: M.E. Sharpe Inc., 1990.

Greene, William H. Econometric Analysis. 2d ed. New York: Macmillan, 1993.

Hakes, David R. "The Objectives and Priorities of Monetary Policy under Different Federal Reserve Chairmen." Journal of Money, Credit and Banking 22 (August 1990): 327-37.

Judd, John P., and Glenn D. Rudebusch. "Taylor's Rule and the Fed: 1970- 1997." Federal Reserve Bank of San Francisco Economic Review 3 (1998): 1-16.

Judge, George G., William E. Griffiths, R. Carter Hill, Helmut Liitkepohl and Tsoung-Chao Lee. The Theory and Practice of Econometrics. New York: John Wiley and Sons, 1985.

Khoury, Salwa S. "The Federal Reserve Reaction Function: A Specification Search." In The Political Economy of American Monetary Policy, edited by Thomas Meyer. Cambridge: Cambridge University Press, 1990.

Maddala, G. S. Limited-Dependent and Qualitative Variables in Economet- rics. Cambridge: Cambridge University Press, 1983.

McNees, Stephen K. "Modeling the Fed: A Forward-Looking Monetary Pol- icy Reaction Function." New England Economic Review (November/De- cember 1986): 3-8.

- - . '% Forward-Looking Monetary Policy Reaction Function: Conti- nuity and Change." New England Economic Review (November/Decem- ber 1992): 3-13.

Meulendyke, Ann-Marie. U.S. Monetary Policy and Financial Markets. Fed- eral Reserve Bank of New York, 1998.

Potts, Glenn T., and Dudley G. Luckett. "Policy Objectives of the Federal Reserve System.'" Quarterly Journal of Economics 92 (August 1978): 525-34.

Romer, Christina D., and David H. Romer. "Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz." In NBER Macro- economics Annual 1989 edited by Olivier Blanchard and Stanley Fischer. Cambridge: MIT Press, 1989.

Salemi, Michael K. "Revealed Preference of the Federal Reserve: Using Inverse Control Theory to Interpret the Policy Equation of a Vector Au- toregression." Journal of Business and Economics Statistics 13 (October 1995): 419-33.

Taylor, John B. "Discretion versus Policy Rules in Practice." Carnegie-Roch- ester Conference Series on Public Policy 39 (1993): 195-214.

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. "An Historical Analysis of Monetary Policy Rules." NBER Working Paper 6768, October 1998.

Tootell, Geoffrey M. B. "Appointment Procedures and F O M C Voting Be- havior." Southern Economic Journal 63 (July 1996): 191-204.

Appendix TABLE A1. Alternative Specifications

Without With Seven Early Lagged

Categories Observations Actions

o/1

112

IX 3

iX4

ot 5

I16

Change in Unemployment Rate

Percentage Change in Industrial Production Percentage Change in Intermediate PPI Percentage Change in Spot Market Prices for Raw Materials Percentage Change in Average Hourly Earnings Lagged Large Decrease

Lagged Small Decrease

Lagged Small Increase

Lagged Large Increase

- 2*loglikelihood

-2.046* -5.367) - 1.394" -4.852) -0.388 -1.527)

2.028* (6,446) 2.440*

(6.274) 3.283*

(5.647) -0.133 -0.135)

0.797* (3.205) 0.619" (2.439) 0.264* (4.061) 1.405

(1.682)

277.327

- 1.983" ( - 3,947)

- 0,919" (-2,136)

1.885" (4,261) 2,169"

(4,298)

- 0,845 ( - 0.625)

0,840" (3,131) 0,183

(0,488) 0.221"

(3,084) - 0.370

( - 0,246)

176.609

- 1.612" ( - 4.661) - 0.549

( - 1.789) 1.969"

(5.369) 2.417" (5.321)

0.257 (0.233) 0.824* (3.211) 0.459

(1.447) 0.239*

(3.719) 1.227

(1.490) - 0.350

( - 0 . 5 6 5 )

- 0.604* ( - 1.993)

1.149" (2.328) 0.081

(0.213) 246.059

NOTE: *significant at the 5% level t-statistics appear in parentheses

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Peter G. VanderHart

T A B L E A2. Alternative Specifications

Curren t Qrt.

Forecasts

Next Qrt.

Forecasts

Taylor Rule

Formula t ion

ct 1 - 0.758 1.739 - 1.800" ( - 0.819) (0.967) ( - 8,335)

ot~ 0.149 2.647 - 0.854* (0.163) (1.464) ( - 6.869)

ct 3 1.937 4.425* 1.250" (1.901) (2.275) (8.768)

a4 2.341" 4.821" 1.581" (2.292) (2.473) (8.992)

Unemployment Rate 0.627 0.355 (0.853) (0.457)

Industrial Production 1.568" 2.755* 0.906* (3.116) (2.919) (4.423)

CPI 3.521 18.302 - 0.001 (0.562) (1.490) ( - 0.002)

- 2 *loglikelihood 176.786 177.146 284.766

NOTE: *significant at the 5% level t-statislScs appear in parentheses

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The Federal Reserve's reaction function under Greenspan: An ordinal probit analysis