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The Asymmetric Effects of Political Pressures on U.S. Monetary Policy
RICHARD T. FROYEN The University of North Carolina, Chapel Hill Chapel Hill, North Carolina
THOMAS HAVRILESKY Duke University Durham, North Carolina
ROGER N. WAUD The University of North Carolina, Chapel Hill Chapel Hill, North Carolina
The Asymmetric Effects of Political Pressures on U.S. Monetary Policy * A number of studies have estimated reaction functions focusing solely on economic state variables such as inflation and unemployment. Other reaction function studies have focused on political measures as regressors. In this study we include both sets of variables to see if political measures are significant when we control for economic state variables. Reaction functions are estimated for the 1959–91 period and for subperiods corresponding to the tenures of different Federal Reserve Board chairmen. We find significant effects for a measure of political influence on the Federal Reserve for the whole period and some subperiods.
The purpose of this paper is to address several questions pertaining to the political economy of U.S. monetary policy by incorporating measures of political pressures along with the usual economic state variables into monetary policy reaction functions. Do political pressures add significant explanatory power over and above that provided by traditional state variables? Or is the statistical significance of such measures in recent research on the political economy of monetary policy explained by an inadequate accounting for economic state variables? Conventional wisdom as espoused by political *We would like to thank Stanley Black, Henry Chappell, Dennis Coates, Milton Friedman, Stephen McNees, Michael Salemi, and Dudley Wallace for their comments and discussions. We thank Jeff Tootel of the Boston Federal Reserve for providing us with the Greenbook forecasts. We are also grateful to two referees for their extensive helpful comments. The usual disclaimer applies. We dedicate this paper to the memory of our co-author Tom Havrilesky, who passed away on September 9, 1995.
Journal of Macroeconomics, Summer 1997, Vol. 19, No. 3, pp. 471–493 Copyright 䉷 1997 by Louisiana State University Press 0164-0704/97/$1.50
471
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud commentators and economists in general, and more rigorously by political business cycle theorists, seems to hold that administrations tend to have a bias for easier monetary policies. Another major question addressed here is: To the extent a bias exists in the United States, either for ease or tightness, how has the Fed, on balance, responded to it? That is, have administration pressures on the Fed, on balance, resulted in a tighter or easier monetary policy than would have otherwise been the case? During the past decade statistical research into the proximate determinants of monetary policy has suggested that political pressures play a more significant role than once thought.1 Until recently economists estimated monetary policy reaction functions by focusing almost exclusively on the role of economic state variables, such as the unemployment and inflation rates, sometimes indirectly acknowledging political influences only to the extent of estimating separate state-variable coefficients over different regimes.2 Khoury (1990) has re-examined much of this work and concluded that the reaction function estimates do not seem very robust across studies. One possible explanation is that these studies do not include explicit measures of the political pressures on monetary policy; that is, there is an omitted variables problem. Several studies have fairly successfully estimated monetary policy reaction functions with the Federal funds rate as the dependent variable and political measures as regressors (see footnote 1). However it is possible that the statistical significance of such measures may be explained by both the monetary authority and political groups reacting to the same (omitted) economic state variables in very similar ways; that is, statistical significance of the political measures may reflect missing state variables such as unemployment and inflation. If estimated reaction functions include both sets of variables and find the political regressors to be significant and robust to various specification tests, then the evidence of significant outside pressures on monetary policy is more compelling, and the implications are significant for issues of monetary reform. We estimate monetary policy reaction functions using monthly data from 1959 through 1991. Reaction functions are estimated for the whole period, 1959–1991, for subperiods corresponding to the tenures of different Fed chairmen, and for various subperiods of particular interest, such as, for example, the “monetarist experiment” of 1979–1982. We choose subperiods according to chairmanship regimes because typically changes in chairmen are accompanied by changes in operating procedures, and because recent 1 See Mayer (1990) and Havrilesky (1995). These works also contain extensive bibliographies on recent research into the political economy of monetary policy. 2 For a fairly extensive bibliography of such studies see Khoury (1990).
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The Asymmetric Effects of Political Pressures research (Hakes 1990) formally testing for breaks indicates that this is the appropriate choice.3 The Federal funds rate is the dependent variable, and the state-of-the-economy variables are forecasts of the inflation rate, the unemployment rate, and the rate of growth of real GNP.4 Our measure of political pressures is the SAFER index, developed by Havrilesky (1995), a measure of executive branch desires for monetary policy.5,6 Section 1 of the paper describes the measure of political pressure on the Fed. Section 2 discusses the reaction function and the forecasting of economic state variables. Reaction function estimates of executive branch (administration) pressure on monetary policy are presented in Section 3, and the Fed’s response to these pressures is assessed in Section 4. Section 5 concludes and summarizes our findings.
1. The Safer Index: Executive Branch Desires There are numerous ways in which the executive branch can communicate its monetary policy desires to the monetary authority. Most communications are not recorded. Therefore, direct documentation is impossible (Havrilesky 1995, Ch. 2). To circumvent these obstacles the index of signaling from Administration to the Federal Reserve (SAFER) is based on the premise that the Administration’s monetary policy intentions are systematically captured in the financial press over time. The index is constructed on the assumption that the financial press efficiently extracts and reports with little bias the content of exchanges between the Administration and Federal Reserve officials. Specifically, it is assumed that the policy content of formal 3 Hakes (1990) finds a break from Martin to Burns and from Burns to Volcker. It is less clear that there are changes in operating procedures from Volcker to Greenspan. Below we present estimates for Greenspan and Volcker separately. We also estimated reaction functions combining the two regimes. 4 It is customary in reaction function studies using the federal funds rate as dependent variable to use the nominal rate, as is done in this study. If the real rate, measured as the nominal rate minus the expected (forecasted) inflation rate, is the dependent variable, adding the expected inflation rate to both sides leaves the nominal funds rate as the dependent variable and the expected inflation rate on the right side of the reaction function—the customary form of the reaction function and the one used here. 5 Havrilesky (1995) focused on the weekly federal funds rate in examining the effects of political/special interest pressures on U.S. monetary policy. This precluded the use of statevariables such as unemployment, the rate of inflation, and GNP as additional explanatory factors since these are not available weekly. Here we focus on monthly data so that we are able to include and control for the effects of these variables. Havrilesky (1995) also examined different issues which dictated the examination of different subperiods than those studied here. 6 The Federal funds rate is often employed as a measure of monetary policy action and a survey of the evidence suggests that it is probably the best measure. See Havrilesky (1995, ch. 6).
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Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud and informal communications from the Administration to the central bank is extracted by the press from speeches, news conferences, press releases, interviews, leaks, etc., and is consistently and reliably reported in the press. It is not assumed that Fed officials must read the financial press to discern Administration desires for monetary policy, but rather that press accounts are a proxy for both formal and informal communications. The SAFER index is based on articles from the Wall Street Journal for the period from January 1952 to November 1991, which mention views of the Administration on monetary policy, whether they emanate from a particular department (e.g., Treasury), official, or spokesperson, or from unidentified Administration sources. Each article was categorized as to whether it signaled a desire for monetary ease, counted as Ⳮ1, monetary tightness, counted as ⳮ1, or neither, counted as zero. The monthly SAFER index used here consists of the simple sum of pluses and minuses for each month.7
2. The Reaction Function The reaction functions estimated in this study are of the form f ⳱ AX* Ⳮ BP Ⳮ cfⳮ1 Ⳮ e ,
(1)
where f is a vector of observed values of the Federal funds rate, X is a matrix of forecasted state variables which the Fed “targets,” P is a matrix of measures of political pressures, and e is a vector of disturbances. Monetary policy is made under conditions of imperfect information. In particular the Fed’s setting of the Federal funds rate depends on forecasts of the current and future levels of state variables, which the monetary authority wishes to affect, or target. Two different approaches are taken here to measure these forecasts of the state variables which condition monetary actions: first, forecasting equations are estimated and used to construct consistent forecasts; second, as an alternative, we use the Green Book forecasts prepared by the Fed staff and presented at each FOMC meeting. We esti7 Research assistants were each assigned two- to four-year periods for scanning microfiche copies of each Wall Street Journal in those years, using the Journal’s “What’s News” digest as a guide and xeroxing and reading all columns and editorials that mentioned the Administration’s views on monetary policy, assigning a Ⳮ1, ⳮ1, or zero to each. Another research assistant independently made another pass through several months of the same microfiche copies in each year and also selected and categorized qualifying articles. The disparity between any two research assistants’ findings were negligible. Finally, Havrilesky independently read and evaluated each article on three, widely separated occasions resulting in the final form of the SAFER index. See Havrilesky (1995, ch. 2) for further details.
474
The Asymmetric Effects of Political Pressures mate reaction functions for both sets of forecasts as a check on the robustness of our findings. The first approach assumes that policymakers forecast “consistently,” which means they employ information efficiently—that is, observations used in forecasting affect the forecast of the variable in the same manner they affect the actual value of the variable. So forecasts take the form X* ⳱ E(X|Xⳮ1) ,
(2)
where Xⳮ1 is the available information set used in forecasting X. The specification of Xⳮ1 employed here consists of lagged values of a number of the major predetermined variables, which almost any macroeconomic model would specify as determinants of the state or target variables, as well as lagged values of the target variables. The predetermined variables used in the forecasting equations are the Federal funds rate, the monetary base, the industrial production index, Moody’s tripleⳮA bond rate, and lagged values of the target variables, the unemployment and inflation rates, all taken from the Citibase tape. The forecasting equations were estimated by regressing the actual values of target variables, using alternatively one, two, and three period lags, on all independent variables. From among these the forecasting equations actually used to generate X* were chosen on the basis of the standard error of regression and the Akaike information criterion. Forecasting equations were estimated using data from the whole period. Alternatively they were estimated using only data from the various subperiods studied and then used to generate X* for the subperiods from which they were estimated. The rationale for this alternative is that the forecast measures relevant for the reaction function are those of the policymakers who know the policy regime (they are its authors). It is therefore credible, and consistent with Hakes’ (1990) findings noted above, that these forecasts are best approximated by forecast equations estimated over each particular regime. A possible concern with using a forecast equation whose coefficients are estimated for a whole regime is that it imputes to the policymaker too much information—the forecast equation is estimated from data that policymakers did not have at the time the forecast was actually made. Were this the case the likely effect would be to bias our results against finding significance for the SAFER index. This follows because, to the extent policymakers are influenced by administration signaling, this effect may be reflected in the future data (such as the monetary base) used to estimate the forecast equation over an entire regime. When such forecasts are used in the reaction function they may already reflect the influence of signaling and therefore mitigate against rev475
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud elation of a significant effect of the SAFER index appearing separately in the reaction function. Given that derivation of the reaction function in a multiperiod framework would have policymakers forecasting target variables for each of several future months and reacting to these forecasts, we assume they respond to the forecast of the average value over these months and accordingly generate three-month average forecasts. We compare the reaction function estimates using these results with those using only one-month forecasts and conclude the three-month forecasts seem the more correct specification. (Both sets of results are compared below.) There are several possible shortcomings of the forecast equation procedure. Regarding the separate forecast equations estimated for each regime, one might not be convinced that FOMC members “know” the regime in the relevant sense of the term. They do not adopt regimes formally, except for the monetarist experiment and its end. Regime changes may evolve more gradually without easily identified, abrupt beginning and ending points such as are implied by the estimated regime-specific forecasting equations. The possibility that the estimated forecast equations embody significant (future) information not in fact possessed by policymakers is another shortcoming, already noted. Another concern is the rigid assumption that policymakers always make a three month ahead forecast (or alternatively, one month ahead). The impression given by the FOMC minutes is that the forecast horizon is vague and variable. Our second approach uses the Green Book forecasts of the state variables in the reaction function. We did this using the one quarter ahead forecasts and, alternatively, using the average of the one-quarter-ahead and the two-quarter-ahead Green Book forecasts.8 One shortcoming of this approach is that the one-quarter-ahead forecasts are only available from 1965 through 1989, and the two-quarter-ahead forecasts only from 1970 through 1987. The forecasting equation approach allowed us to estimate and examine reaction functions over a longer period. Another possible shortcoming of using the Green Book forecasts is that FOMC members may rely to varying degrees on other forecasts—Fed Bank presidents have forecasts from their own staffs, for example. This is a consideration that may favor the forecasting equation approach which assumes that FOMC members, like other economic agents, form rational expectations based on all available information. 8
Green Book forecasts are as of a given FOMC meeting date. A monthly series was created as a weighted average of meeting date forecasts. If, for example, the FOMC met on (11/15/75) and (12/15/75), the (12/75) observation would be 0.5 times each meeting date’s forecast. The assumption implicit in this calculation is that the forecast that conditioned the FOMC directive to the Open Market Desk continued to be that from the previous meeting until a subsequent meeting was held.
476
The Asymmetric Effects of Political Pressures 3. Estimates of Executive Branch Pressures on Monetary Policy Here we report the results obtained estimating reaction functions measuring executive branch desires via the SAFER index S.9 The state variables which the monetary authority is assumed to target are the unemployment rate and the rate of inflation in the consumer price index. Forecasts of the state variables are measured in two ways as described in the previous section: from estimated forecast equations and by Green Book forecasts. When we consider the Green Book forecasts we add a third state variable, real GNP growth. We begin by considering reaction function estimates using the fitted values from forecast equations. Reaction Function Estimates with Fitted Values from Forecast Equations The specific forms of (1) estimated here are ˙ t Ⳮ ␣3St Ⳮ ␣4 ftⳮ1 Ⳮ et ft ⳱ ␣0 Ⳮ ␣1U* t Ⳮ ␣2P*
(3)
˙ t Ⳮ ␣5SEt Ⳮ ␣6STt Ⳮ ␣4 ftⳮ1 Ⳮ et , ft ⳱ ␣0 Ⳮ ␣1U* t Ⳮ ␣2P*
(4)
and
˙ t are the forecasts of the unemployment and inflation rates where U*t and P* respectively, St is the value of the SAFER index in period t, and SEt and STt are SAFER signals for ease and tightness, respectively. It should be emphasized that a monetary aggregate enters these reaction functions since the monetary base is one of the variables in the forecasting equations for ˙ . Similarly the industrial production index and the triple-A bond U* t and P* t rate enter the reaction functions in the same way. The basic premise of our procedure however is that these variables enter in a way that reflects their influence on the inflation and unemployment targets.10 Table 1 shows the reaction function estimates when forecasts of the state variables are fitted values from forecast equations. The time period over which (3) and (4) are estimated is 1959:5 through 1991:10. The starting point precedes 1961, the year when executive branch attempts to influence 9
Havrilesky (1995) also reports results using a “threat-augmented SAFER” index which is a combined measure of executive and congressional pressures on the Federal Reserve. In an earlier version of the paper we found that results using SAFER and the threat-augmented SAFER differed only slightly. Therefore, here we confine ourselves to analysis of the effects of executive branch signaling. 10 Residuals from initial OLS estimates were tested for autocorrelation using the Durbin hstatistic. Since there was evidence of autocorrelation for the period as a whole and almost all subperiods, the regressions were reestimated using Hatanaka’s two-step procedure.
477
478
TABLE 1. Reaction Function Estimates with Fitted Values of Forecast Equations U*
P˙*
S
0.303 (1.90)
ⳮ0.004 (ⳮ0.156)
80.44 (4.37)
ⳮ0.103 (ⳮ3.79)
0.304 (1.60)
ⳮ0.007 (ⳮ0.210)
84.66 (4.08)
0.380 (1.34)
ⳮ0.050 (ⳮ1.22)
51.76 (2.19)
0.472 (1.68)
ⳮ0.066 (ⳮ1.62)
41.63 (1.77)
1.85 (3.16)
ⳮ0.200 (ⳮ2.83)
124.35 (3.66)
1.82 (3.12)
ⳮ0.192 (ⳮ2.76)
123.0 (3.65)
Time Period
Const
59:5–91:10
59:5–70:1
70:2–79:9
SE
ⳮ0.057 (ⳮ1.58)
ST
ⳮ0.159 (ⳮ3.49)
ⳮ0.062 (ⳮ1.58) 0.025 (0.471)
ⳮ0.167 (ⳮ2.97)
ⳮ0.012 (ⳮ0.318) 0.030 (0.681)
ⳮ0.105 (ⳮ1.56)
ftⳮ1
q/R¯2
DW
0.919 (56.73)
0.60 0.95
2.23
0.913 (47.42)
0.35 0.96
1.90
0.950 (36.55)
ⳮ0.01 0.97
2.04
0.943 (36.71)
0.04 0.97
2.11
0.813 (17.25)
0.50 0.96
1.92
0.808 (17.23)
0.50 0.96
1.93
79:10–82:6
79:8–87:8
87:9–91:10
ⳮ2.61 (ⳮ0.198)
0.169 (0.186)
149.8 (0.723)
ⳮ2.31 (ⳮ0.162)
0.187 (0.174)
123.71 (0.549)
0.783 (0.709)
0.090 (0.631)
200.86 (2.95)
0.779 (0.664)
0.096 (0.634)
203.12 (2.86)
3.81 (2.51)
ⳮ0.454 (ⳮ2.75)
24.71 (0.803)
4.02 (2.66)
ⳮ0.474 (ⳮ2.88)
22.17 (0.720)
ⳮ0.280 (ⳮ1.59) ⳮ0.631 (ⳮ1.92)
ⳮ0.130 (ⳮ0.667)
ⳮ0.167 (ⳮ2.48) ⳮ0.187 (ⳮ1.85)
ⳮ0.140 (ⳮ1.42)
ⳮ0.032 (ⳮ1.34) ⳮ0.034 (ⳮ1.40)
0.056 (0.567)
0.998 (1.96)
0.29 0.63
1.81
0.995 (1.82)
0.26 0.61
1.60
0.772 (11.34)
0.44 0.90
1.95
0.776 (10.82)
0.36 0.90
1.79
0.829 (10.20)
0.46 0.97
1.94
0.820 (10.15)
0.44 0.97
1.96
479
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud monetary policy began in earnest (see Havrilesky 1995, ch. 2).11 The results for the whole period reveal the estimated coefficient for the inflation rate to be statistically significant while the estimated coefficient for the unemployment rate is not. The estimated coefficient for the SAFER index is also significant and has the correct sign. Estimation of (4) indicates that tightness signaling drives this significance, though the index of signals for ease has a marginal significance level of 0.11. The estimates for the Martin chairmanship, 1959:4–1970:1, closely resemble those for the whole period. The estimated coefficient for the inflation rate is statistically significant while the coefficient for the unemployment rate is not. In addition, the estimated coefficient for tightness signals is statistically significant, but the coefficient for ease signals is not. Estimation of (3) and (4) for the Burns-Miller era, 1970:2–1979:9, show the estimated coefficient for both the unemployment rate and the inflation rate variables to be statistically significant.12 The SAFER index is not significant for this subperiod either when entered directly or when broken into ease and tightness signals. The measure of tightness does, however, have a marginal significance level of 0.12. The results for the Volcker chairmanship, 1979:10–1987:8, show a significant coefficient for the inflation rate but not for the unemployment rate. The SAFER index is statistically significant and during this period it appears that the response was more to ease signals (marginal significance 0.07) than to those for tightness (0.16). During the Greenspan period, 1987:9–1991:10, the unemployment rate is significant but the inflation rate is not. The SAFER index is not significant, but the marginal significance level of the index of tightness signals (0.16) is greater than that for ease signals. Finally, because of its much publicized uniqueness, (3) and (4) were estimated for that subperiod of the Volcker regime corresponding to the socalled “monetarist experiment,” 1979:10–1982:6, when the Federal Reserve targeted on the money supply rather than the Federal funds rate.13 (Recall 11
There was no executive branch signaling in 1959 and 1960. In fact, the 1950s were in general a period of very little signaling, reflective of Eisenhower’s hands-off attitude toward monetary policy. Signaling began in earnest with the advent of the Kennedy Administration in 1961 and, with the exception of the Ford interregnum, escalated to considerably higher levels during the 1970s and 1980s. 12 The tenure of G. William Miller as chairman of the Fed extended from March 1978 through July 1979. This was deemed too short a period to be treated separately. The first months of Volcker’s term, prior to the October shift in policy procedures, were also considered as part of the 1970s regime. 13 The appropriate endpoint for the monetarist experiment is unclear. We have estimated reaction functions for this subperiod with the alternative endpoint 1982:9. This change did not result in a significant change in our results. With either end point, however, it should be noted
480
The Asymmetric Effects of Political Pressures that the monetary base enters the reaction function via the forecasting equa˙ ). Perhaps because of the paucity of data for this subtions for U*t and P* t period, estimated coefficients for neither the unemployment nor the inflation rate are statistically significant. The estimated coefficient for the SAFER index is not significant in specification (3). When SAFER is separated into ease and tightness signals in specification (4), the measure of ease signals is significant at the 10% level consistent with the estimate for the whole Volcker period. The equations used to generate the forecasts employed in Table 1 were estimated from data for the whole period covered by our study. The results in Table 1, however, indicate that there were quite different reaction functions during different Federal Reserve chairmanships, a result also found by Hakes (1990). If there were different policy regimes, then there may have been shifts in the coefficients of the reduced form equations that characterize the economy—the gist of the Lucas critique. To check for this possibility and its effect on the reaction functions estimated for the subperiods reported in Table 1, we estimated separate forecasting equations for each subperiod using only data for that subperiod. We then re-estimated the reaction functions for each subperiod using fore˙ generated by the forecasting equation estimated specific casts of U*t and P* t to that subperiod. These results are shown in Table 2. Comparing them with those in Table 1 reveals no significant differences. As another check of the sensitivity of our results to the specification of the forecast equation, we also estimated forecast equations using one month ahead forecasts of U*t and ˙ . Reaction function estimates using these forecasts did not differ signifiP* t cantly from those in Tables 1 and 2. Reaction Functions Estimated with Green Book Forecasts Tables 3–5 show reaction function estimated with the state of the economy variables measured by Green Book forecasts. The estimates in Table 3 parallel those in Table 1 except for the shortened time periods due to lack of availability of Green Book forecasts for some years. Table 4 shows estimates with an additional state variable, the rate of real GNP growth (Y˙*). Table 5 tests the effect of extending the forecast horizon from 3 to 6 months. Comparing the results in Table 3 with those in Table 1, there are a number of differences in the estimated coefficients on the state variables, but little difference in the estimated effects of the signaling indices. With the exception of the Burns chairmanship, the pattern of coefficients on the state variables is similar in the two tables but in a number of that there are not many observations for this subperiod, and as a consequence our estimates will be imprecise.
481
TABLE 2. Reaction Function Estimates: Regime Specific Forecast Equations 482
Time Period 59:5–70:1
70:2–79:9
79:10–82:6
79:8–87:8
87:9–91:10
U*
P˙*
S
0.400 (1.35)
ⳮ0.052 (ⳮ1.19)
52.82 (1.74)
ⳮ0.062 (ⳮ1.57)
0.488 (1.67)
ⳮ0.067 (ⳮ1.57)
41.43 (1.39)
1.95 (3.59)
ⳮ0.215 (ⳮ3.33)
107.96 (2.36)
1.92 (3.55)
ⳮ0.211 (ⳮ3.26)
108.14 (2.38)
ⳮ22.22 (ⳮ1.48)
1.21 (1.14)
218.0 (1.06)
ⳮ11.52 (ⳮ0.772)
0.768 (0.639)
142.76 (0.645)
0.557 (0.494)
0.108 (0.748)
217.28 (3.43)
0.557 (0.467)
0.111 (0.720)
217.02 (3.31)
Const
3.84 (2.29)
ⳮ4.49 (ⳮ2.49)
25.66 (0.717)
4.03 (2.40)
ⳮ0.468 (ⳮ2.60)
22.94 (0.64)
SE
0.028 (0.522)
ST
ⳮ0.169 (ⳮ3.01)
ⳮ0.032 (ⳮ0.886) 0.001 (0.026)
ⳮ1.04 (ⳮ1.55)
ⳮ0.339 (ⳮ2.03) ⳮ0.728 (ⳮ2.27)
ⳮ0.100 (ⳮ0.547)
ⳮ0.166 (ⳮ2.52) ⳮ0.183 (ⳮ1.84)
ⳮ0.144 (ⳮ1.49)
ⳮ0.036 (ⳮ1.49) ⳮ0.038 (ⳮ1.57)
0.052 (0.532)
ftⳮ1
q/R¯2
DW
0.948 (33.3)
ⳮ0.01 0.97
2.01
0.942 (33.90)
0.04 0.97
2.07
0.829 (15.47)
0.48 0.96
1.96
0.824 (15.38)
0.97 0.96
1.96
1.72 (3.23)
0.24 0.59
1.20
1.29 (2.64)
0.27 0.54
1.38
0.775 (11.67)
0.38 0.91
1.85
0.776 (11.22)
0.30 0.91
1.71
0.823 (9.22)
0.42 0.97
1.89
0.815 (9.15)
0.39 0.97
1.92
The Asymmetric Effects of Political Pressures instances the Green Book forecasts have lower levels of significance. For example, the whole period estimate for the inflation variable is significant at the 1% level in Table 1, but only at the 10% level in Table 3. For the Burns chairmanship, neither the Green Book forecast of inflation or unemployment is significant in Table 3, while forecasts of both are significant in Table 1. With respect to the SAFER indices, for the whole period in Table 3 both measures of ease and tightness signals are significant at the 5% level, while only tightness signals were significant in Table 1. For the Martin period the coefficient on the SAFER index is significant while it was not in Table 1. Still, when SAFER is split into ease and tightness signals the pattern is the same in both Tables—only the index of tightness signals is significant. Otherwise, the pattern of estimated coefficients on the signaling indices does not differ significantly between the two tables. When we add real GNP growth (Y˙*) as an additional state variable in Table 4, it enters significantly for the whole period and for several of the subperiods. Inclusion of this additional variable does not, however, result in significant changes (relative to the estimates in Table 3) in the estimated pattern of responses to the signaling indices except during the period of the monetarist experiment. During that period the SAFER index is now significant only at the 10% level, and when SAFER is broken into ease and tightness signals neither measure is significant. Finally, Table 5 shows estimates where the Green Book forecasts are for a 6-month horizon. Given available data for these forecasts, estimates are for only three of the subperiods. A comparison with Table 4 (which is the relevant table because Y˙* is included in the estimates in Table 5) reveals that this change in forecast horizon has no dramatic effect on our estimates.14,15 14
In an earlier version of the paper, we also addressed the robustness issue by estimating equations that varied the specification of the state of the economy variables. One specification added first differences of the state variables. Another, a variant of the Ramsey (1969) specification test, added squared values of the state variables. Estimated coefficients on our indexes of executive branch signaling were not sensitive to these changes in specification. Our tests for non-linearity (the Ramsey tests) produced the same results as Hakes and Gamber (1992). 15 We have considered the possibility that SAFER is responding to the Federal funds rate, rather than the other way around. For example, if the Administration were to respond to a rise (fall) in the Federal funds rate by signaling for ease (tightness), then the Federal funds rate and SAFER would be positively correlated, and one would expect the signs on S in Tables 1–5 to be positive. The coefficients on SAFER in the tables are, however, almost always negative. All the significant coefficients are negative, as would be predicted if causality were to run only from SAFER to the Federal funds rate. However, what if at times the Federal funds rate responds to SAFER, while at other times SAFER responds to the rate? Then in the former case the sign would be negative and in the latter, positive, and on balance the estimated coefficients on SAFER might appear insignificant. We cannot rule out this possibility. Thus, while possible
483
484
TABLE 3. Reaction Function Estimates: Green Book Forecasts Time Period 66:5–89:12
66:5–70:1
70:2–79:9
Const
U*
P˙*
0.439 (1.41)
0.015 (0.328)
0.069 (1.86)
0.443 (1.43)
0.011 (0.232)
0.067 (1.81)
1.67 (1.50)
ⳮ0.456 (ⳮ1.70)
0.117 (2.36)
1.70 (1.53)
ⳮ0.448 (ⳮ1.67)
0.117 (2.37)
0.682 (0.956) 0.735 (1.05)
ⳮ0.029 (ⳮ0.392) ⳮ0.038 (ⳮ0.522)
0.061 (1.13) 0.059 (1.12)
S
SE
ftⳮ1
q/R¯2
DW
0.893 (25.21)
0.46 0.93
1.90
0.895 (24.92)
0.48 0.92
1.95
0.942 (32.78)
0.20 0.98
2.05
ⳮ0.300 (ⳮ3.89)
0.925 (29.84)
0.17 0.98
2.00
0.51 0.95 0.51 0.96
1.86
ⳮ0.097 (ⳮ1.39)
0.876 (10.46) 0.736 (1.05)
ST
ⳮ0.120 (ⳮ3.66) ⳮ0.098 (ⳮ2.24)
ⳮ0.153 (ⳮ2.86)
ⳮ0.212 (ⳮ3.67) ⳮ0.097 (ⳮ1.08) 0.002 (0.042) 0.044 (0.995)
1.89
79:10–82:6
79:8–87:8
87:9–89:12
ⳮ19.50 (ⳮ1.70)
ⳮ0.576 (ⳮ0.892)
0.020 (0.084)
ⳮ0.897 (ⳮ0.083)
ⳮ0.379 (ⳮ0.499)
0.032 (0.118)
1.25 (1.70)
ⳮ0.071 (ⳮ0.387)
0.131 (0.995)
1.34 (0.802)
ⳮ0.058 (ⳮ0.314)
0.147 (1.12)
12.09 (3.16)
ⳮ1.60 (ⳮ3.01)
0.002 (0.027)
11.97 (2.87)
ⳮ1.58 (ⳮ2.70)
ⳮ0.004 (ⳮ0.048)
ⳮ0.386 (ⳮ2.38) ⳮ0.685 (ⳮ2.07)
ⳮ0.113 (ⳮ0.608)
ⳮ0.156 (ⳮ2.43) ⳮ0.215 (ⳮ2.27)
ⳮ0.097 (ⳮ1.05)
ⳮ0.037 (ⳮ1.33) ⳮ0.038 (ⳮ1.35)
ⳮ0.039 (ⳮ0.386)
2.61 (3.69)
0.51 0.18
1.33
1.25 (2.54)
0.53 0.13
1.79
0.864 (6.07)
0.48 0.80
1.94
0.845 (5.91)
0.49 0.79
1.89
0.601 (4.32)
0.59 0.93
1.93
0.607 (3.94)
0.55 0.94
1.85
485
486
TABLE 4. Reaction Function Estimates: Green Book Forecast Including Y* Time Period
Const
66:5–89:12
0.193 (0.597)
0.010 (0.229)
0.197 (0.611)
0.059 (0.134)
66:5–70:1
70:2–79:9
U*
P˙*
Y*
S
0.076 (2.12)
0.037 (2.01)
ⳮ0.122 (ⳮ3.74)
0.074 (2.08)
0.037 (2.01)
1.28 (1.09)
ⳮ0.039 (ⳮ1.41)
0.096 (1.76)
0.025 (0.869)
1.32 (1.13)
ⳮ0.381 (ⳮ0.139)
0.098 (1.80)
0.025 (0.833)
0.285 (0.422)
ⳮ0.026 (ⳮ0.369)
0.054 (1.04)
0.024 (1.61)
0.372 (0.544)
ⳮ0.031 (ⳮ0.448)
0.053 (1.03)
0.021 (1.43)
SE
ⳮ0.101 (ⳮ2.32)
ST
ⳮ0.153 (ⳮ2.86)
ⳮ0.219 (ⳮ3.69) ⳮ0.104 (ⳮ1.12)
ⳮ0.307 (ⳮ3.89)
ⳮ0.002 (ⳮ0.045) 0.039 (0.839)
ⳮ0.092 (ⳮ1.31)
ftⳮ1
q/R¯2
DW
0.910 (26.51)
0.42 0.93
1.89
0.912 (26.46)
0.43 0.93
1.93
0.963 (27.02)
0.19 0.98
2.08
0.944 (25.08)
0.15 0.98
2.01
0.926 (13.00)
0.41 0.95
1.78
0.915 (12.83)
0.41 0.96
1.76
79:10–82:6
79:8–87:8
87:9–89:12
9.94 (2.14)
ⳮ0.130 (ⳮ0.356)
ⳮ0.182 (ⳮ1.06)
0.387 (3.76)
10.01 (1.98)
ⳮ0.115 (ⳮ0.292)
ⳮ0.178 (ⳮ0.978)
0.378 (3.58)
0.944 (6.14)
ⳮ0.062 (ⳮ0.365)
0.208 (1.74)
0.107 (2.06)
1.03 (0.670)
ⳮ0.048 (ⳮ0.281)
0.228 (1.92)
0.108 (2.07)
7.71 (3.04)
ⳮ1.03 (ⳮ3.00)
0.085 (1.21)
0.147 (3.02)
8.01 (2.74)
ⳮ1.07 (ⳮ2.66)
0.086 (1.16)
0.151 (2.97)
ⳮ0.302 (ⳮ1.81) ⳮ0.504 (ⳮ1.59)
ⳮ0.194 (ⳮ0.950)
ⳮ0.164 (ⳮ2.58) ⳮ0.227 (ⳮ2.40)
ⳮ0.103 (ⳮ1.11)
ⳮ0.026 (ⳮ0.881) ⳮ0.026 (ⳮ0.867)
ⳮ0.060 (ⳮ0.564)
0.511 (3.60)
0.09 0.74
2.09
0.507 (3.33)
0.12 0.74
1.96
0.827 (6.90)
0.32 0.91
1.78
0.805 (6.79)
0.33 0.91
1.72
0.668 (7.82)
0.31 0.97
2.09
0.660 (6.89)
0.28 0.96
2.03
487
488
TABLE 5. Reaction Function Estimates: Green Book Forecasts, Two-Quarter Forecast Horizon Time Period
Const
U*
P˙*
Y*
S
70:2–79:9
0.204 (0.231)
ⳮ0.057 (ⳮ0.602)
0.085 (1.10)
0.043 (1.59)
ⳮ0.001 (ⳮ0.018)
0.303 (0.339)
ⳮ0.059 (ⳮ0.618)
0.088 (1.12)
0.038 (1.39)
12.04 (2.33)
ⳮ0.512 (ⳮ1.36)
0.045 (0.219)
0.635 (4.45)
12.18 (2.25)
ⳮ0.490 (ⳮ1.24)
0.053 (0.248)
0.646 (4.42)
0.652 (0.628)
ⳮ0.055 (ⳮ0.467)
0.513 (4.25)
0.237 (3.34)
0.654 (0.631)
ⳮ0.045 (ⳮ0.380)
0.525 (4.38)
0.240 (3.37)
79:10–82:6
79:8–87:8
SE
0.039 (0.854)
ST
ⳮ0.090 (ⳮ1.28)
ⳮ0.301 (ⳮ1.96) ⳮ0.535 (ⳮ1.86)
ⳮ0.175 (ⳮ0.890)
ⳮ0.193 (ⳮ3.05) ⳮ0.238 (ⳮ2.46)
ⳮ0.150 (ⳮ1.57)
ftⳮ1
q/R¯2
DW
0.930 (10.15)
0.45 0.95
1.91
0.913 (9.93)
0.44 0.95
1.86
0.457 (3.50)
0.06 0.77
2.07
0.445 (3.31)
0.11 0.78
2.02
0.674 (7.86)
0.36 0.92
1.84
0.665 (7.85)
0.37 0.91
1.81
The Asymmetric Effects of Political Pressures 4. Fed Response to Administration Pressures It was noted in the introduction that conventional wisdom among political commentators and economists generally seems to hold that political administrations tend to have a bias for easier monetary policy. Indeed this impression seems consistent with the fact that over the whole period studied here, 1959:4–91:10, there were 207 ease signals and 134 for tightness. There is considerable variation across regimes in the preponderance of signal type however, with tightness signals sometimes dominant. The question is, Whatever the administration bias in a particular regime, how has the Fed responded to it? For the whole period 1959–1991, the results in Table 1 using forecast equations suggest that the Fed was more responsive to tightness signals. For the shorter whole period 1965–1989 the results in Tables 3 and 4 using Green Book forecasts suggest the same, though less strongly—the tightness coefficient estimates are roughly 50% larger than the ease coefficients in the latter two tables, about 150% larger in Table 1. Our finding of a stronger Fed response to tightness signals over the whole period does not mean the Fed generally has a preference for more ease relative to the administration, or put the other way, that the administration generally has a preference for more tightness relative to the Fed. Relative preferences are revealed by what signals are sent. At those times when an administration signals for ease it prefers more ease than the Fed, and at those times when it signals for tightness it prefers more tightness than the Fed. Our findings only indicate that over the whole period the Fed was more responsive to tightness signals. This however masks considerable differences across regimes. During the Martin regime, 1959:4–70:1, the estimates in Tables 1 and 2 indicate a significant Fed response to tightness signals but not to ease. The portion of the Martin years, 1965:5–70:1, we were able to examine using Green Book forecasts. Tables 3 and 4 indicate the same thing. Over the whole Martin regime there were 25 ease signals and 23 tightness signals, while for the 1965:5–70:1 portion there were 6 ease versus 11 tightness signals. For the Martin regime administration pressures were relatively balanced between ease and tightness. Since the Fed seems to have responded only to administration signals for tightness, on balance this suggests that during the Martin years monetary policy was tighter than it would have been in the absence of pressure from the executive branch. For the Burns regime, 1970:2–79:9, none of the results in Tables 1–5 reverse causation would not lead us to incorrectly conclude that SAFER was a significant influence on the Federal funds rate, it could result in our not finding a significant effect for SAFER when in fact there was one. The Ramsey (1969) specification tests referred to in the previous footnote, however, did not indicate the presence of such reverse causation.
489
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud indicate a statistically significant response to signaling, though Tables 1 and 2 weakly suggest some responsiveness to tightness signals. During the Burns years there were 57 ease signals versus 24 tightness signals, but apparently with little effect on monetary policy. Over this regime administration preferences do seem biased toward ease. During the monetarist experiment years, 1979:10–82:6, of the Volcker regime the results in Tables 1–5 all suggest that the Fed only responded to ease signals. This despite the fact there were many more tightness signals, 30, than ease signals, 15, suggesting that on balance administration preferences tilted towards tightness over this period. For the whole Volcker regime, 1979:8–87:8, the results in Tables 1–5 indicate the same pattern of response. There were 67 ease signals and 54 tightness signals during this period, indicating a slight tilt of administration preference towards ease overall. The Fed’s response suggests that monetary policy was easier than might otherwise have been the case in the absence of administration pressure, both during the monetarist experiment years and over the whole Volcker regime. Nonetheless, the significant response of the Fed funds rate to inflation during this period indicates inflation fighting was a priority. The significant response to ease signals, however, suggests Fed policy might have been even more anti-inflationary but for administration pressure. The Fed does not appear to have responded to signaling during the early Greenspan years, 1987:9–91:10, Tables 1 and 2, though the response to ease signaling in Table 2 shows some weak significance. Administration preferences were very skewed towards ease—43 ease versus 3 tightness signals during the period. The same story holds for the Green Book forecast results, Tables 3 and 4, for the shorter period 1987:9–89:12. A final question we consider is that of the quantitative effect of administration signaling. How large is the effect of signaling on the federal funds rate? Clearly, this depends on the amount of signaling and the effect per signal as estimated by coefficients on SAFER. Table 6 provides some illustrative evidence on the quantitative importance of signaling for time periods where there was statistically significant evidence of an effect. For each month (during these intervals) when there were two or more signals the table shows the effect on the federal funds rate measured in percentage points. For other months where there was one signal, the estimated effect is one half the smallest number in the table for that period. The numbers in columns (1) and (3) are calculated using estimated coefficients from Tables 1 and 3, respectively. For the monetarist subperiod (1979:10–1982:6) the table gives two sets of estimates—one based on the coefficients from the reaction function estimated for that subperiod and one based on the coefficient from the estimate for the whole Volcker period. 490
TABLE 6. Impact Effect of Administration Signaling
Martin Period 07/63 02/65 02/69 05/69
Volcker (1979:10–1982:6) 04/80 05/80 10/80 10/81
Volcker (1979:8–1987:8) 04/80 05/80 10/80 10/81 08/82 10/82 11/82 01/83 11/83 12/83 02/84 05/84 11/84 12/84 02/85
(1)
(3)
0.334 0.334 0.334 0.334
0.600 0.600 0.600 0.600
(1)
(3)
ⳮ1.262 ⳮ1.262 ⳮ1.262 ⳮ1.893
ⳮ1.370 ⳮ1.370 ⳮ1.370 ⳮ2.055
(1)
(3)
ⳮ0.374 ⳮ0.374 ⳮ0.374 ⳮ0.561 ⳮ0.374 ⳮ0.748 ⳮ0.374 ⳮ0.374 ⳮ0.561 ⳮ0.561 ⳮ0.748 ⳮ0.748 ⳮ0.561 ⳮ0.374 ⳮ0.374
ⳮ0.430 ⳮ0.430 ⳮ0.430 ⳮ0.645 ⳮ0.645 ⳮ0.860 ⳮ0.430 ⳮ0.430 ⳮ0.645 ⳮ0.645 ⳮ0.860 ⳮ0.860 ⳮ0.645 ⳮ0.430 ⳮ0.430
NOTE: The numbers on the table give the impact effect of administration signaling on the Federal funds rate measured in percentage points for months where there were 2 or more signals. Results are shown only where there was statistically significant evidence of the effects of signaling. The column labeled (1) uses the coefficient estimate on the relevant SAFER measure from Table 1. The column labeled (3) uses the coefficient from Table 3.
491
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud One point to note is that the numbers in the table give only the impact effects of signaling. Since our reaction functions are based on a partial adjustment mechanism (they include a lagged dependent variable), the effects of signals persist over time. Thus, in periods, such as late 1982 and early 1983, when signals “bunch up,” the effects in some months will be much larger than the numbers shown in the table. Overall, the estimated effect of signaling, as illustrated by the numbers in Table 6, suggest that administration pressures on the Fed have from time to time had a substantial impact on the Fed funds rate. 5. Conclusion We began with the question of whether political pressures add significant explanatory power in reaction functions which include economic state variables and, if so, what effect such pressures had on the stance of monetary policy. Our estimates for the whole period indicate that our measure of political pressures (SAFER) does have significant explanatory power. These estimates also indicate that the effect on monetary policy of executive branch signaling has been to produce a tighter policy stance, even though administration signals for ease were more frequent than for tightness. Our estimates for separate Federal Reserve chairmans’ tenure, however, show considerable variation in the significance of our measure of political pressures and of the direction of its effect on the ease or tightness of monetary policy. Our measure of political pressures is statistically significant in the Martin and Volcker chairmanships. In the former case signaling appears to have resulted in a tighter monetary policy stance, while in the latter case it had the opposite effect. Evidence for any effect of signaling is weak for the Burns and Greenspan periods. What evidence there is suggests signaling tilted policy toward tightness in the Burns period and ease in the Greenspan period. The hypothesis that signaling had no effect in these periods cannot, however, be rejected at conventional levels of statistical significance. However, taken together, the weak evidence for signaling in the Burns and Greenspan periods, and the strong evidence for the Martin and Volcker chairmanships, indicate that signaling resulted in a tighter monetary policy stance pre-1980, while post-1980 it tilted policy toward ease. Received: November 1994 Final version: May 1996
References Hakes, David. “The Objectives and Priorities of Monetary Policy under Different Federal Reserve Chairman.” Journal of Money, Credit, and Banking 22 (August 1990): 327–37. 492
The Asymmetric Effects of Political Pressures Hakes, David, and Edward Gamber. “Does the Federal Reserve Respond to Errant Money Growth? Evidence from Three Monetary Regimes.” Journal of Money, Credit, and Banking 24 (February 1992): 127–34. Havrilesky, Thomas. The Pressures on American Monetary Policy. Boston: Kluwer Academic Publishers, 1995. Khoury, Salwa. “The Federal Reserve Reaction Function: A Specification Search.” In The Political Economy of American Monetary Policy, edited by Thomas Mayer. Cambridge: Cambridge University Press, 1990. Mayer, Thomas, ed. The Political Economy of American Monetary Policy. Cambridge: Cambridge University Press, 1990. Ramsey, James B. “Tests for Specification Errors in Classical Linear Least Squares Regression Analysis.” Journal of the Royal Statistical Society Series B 31 (1969): 350–71.
493
THOMAS HAVRILESKY Duke University Durham, North Carolina
ROGER N. WAUD The University of North Carolina, Chapel Hill Chapel Hill, North Carolina
The Asymmetric Effects of Political Pressures on U.S. Monetary Policy * A number of studies have estimated reaction functions focusing solely on economic state variables such as inflation and unemployment. Other reaction function studies have focused on political measures as regressors. In this study we include both sets of variables to see if political measures are significant when we control for economic state variables. Reaction functions are estimated for the 1959–91 period and for subperiods corresponding to the tenures of different Federal Reserve Board chairmen. We find significant effects for a measure of political influence on the Federal Reserve for the whole period and some subperiods.
The purpose of this paper is to address several questions pertaining to the political economy of U.S. monetary policy by incorporating measures of political pressures along with the usual economic state variables into monetary policy reaction functions. Do political pressures add significant explanatory power over and above that provided by traditional state variables? Or is the statistical significance of such measures in recent research on the political economy of monetary policy explained by an inadequate accounting for economic state variables? Conventional wisdom as espoused by political *We would like to thank Stanley Black, Henry Chappell, Dennis Coates, Milton Friedman, Stephen McNees, Michael Salemi, and Dudley Wallace for their comments and discussions. We thank Jeff Tootel of the Boston Federal Reserve for providing us with the Greenbook forecasts. We are also grateful to two referees for their extensive helpful comments. The usual disclaimer applies. We dedicate this paper to the memory of our co-author Tom Havrilesky, who passed away on September 9, 1995.
Journal of Macroeconomics, Summer 1997, Vol. 19, No. 3, pp. 471–493 Copyright 䉷 1997 by Louisiana State University Press 0164-0704/97/$1.50
471
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud commentators and economists in general, and more rigorously by political business cycle theorists, seems to hold that administrations tend to have a bias for easier monetary policies. Another major question addressed here is: To the extent a bias exists in the United States, either for ease or tightness, how has the Fed, on balance, responded to it? That is, have administration pressures on the Fed, on balance, resulted in a tighter or easier monetary policy than would have otherwise been the case? During the past decade statistical research into the proximate determinants of monetary policy has suggested that political pressures play a more significant role than once thought.1 Until recently economists estimated monetary policy reaction functions by focusing almost exclusively on the role of economic state variables, such as the unemployment and inflation rates, sometimes indirectly acknowledging political influences only to the extent of estimating separate state-variable coefficients over different regimes.2 Khoury (1990) has re-examined much of this work and concluded that the reaction function estimates do not seem very robust across studies. One possible explanation is that these studies do not include explicit measures of the political pressures on monetary policy; that is, there is an omitted variables problem. Several studies have fairly successfully estimated monetary policy reaction functions with the Federal funds rate as the dependent variable and political measures as regressors (see footnote 1). However it is possible that the statistical significance of such measures may be explained by both the monetary authority and political groups reacting to the same (omitted) economic state variables in very similar ways; that is, statistical significance of the political measures may reflect missing state variables such as unemployment and inflation. If estimated reaction functions include both sets of variables and find the political regressors to be significant and robust to various specification tests, then the evidence of significant outside pressures on monetary policy is more compelling, and the implications are significant for issues of monetary reform. We estimate monetary policy reaction functions using monthly data from 1959 through 1991. Reaction functions are estimated for the whole period, 1959–1991, for subperiods corresponding to the tenures of different Fed chairmen, and for various subperiods of particular interest, such as, for example, the “monetarist experiment” of 1979–1982. We choose subperiods according to chairmanship regimes because typically changes in chairmen are accompanied by changes in operating procedures, and because recent 1 See Mayer (1990) and Havrilesky (1995). These works also contain extensive bibliographies on recent research into the political economy of monetary policy. 2 For a fairly extensive bibliography of such studies see Khoury (1990).
472
The Asymmetric Effects of Political Pressures research (Hakes 1990) formally testing for breaks indicates that this is the appropriate choice.3 The Federal funds rate is the dependent variable, and the state-of-the-economy variables are forecasts of the inflation rate, the unemployment rate, and the rate of growth of real GNP.4 Our measure of political pressures is the SAFER index, developed by Havrilesky (1995), a measure of executive branch desires for monetary policy.5,6 Section 1 of the paper describes the measure of political pressure on the Fed. Section 2 discusses the reaction function and the forecasting of economic state variables. Reaction function estimates of executive branch (administration) pressure on monetary policy are presented in Section 3, and the Fed’s response to these pressures is assessed in Section 4. Section 5 concludes and summarizes our findings.
1. The Safer Index: Executive Branch Desires There are numerous ways in which the executive branch can communicate its monetary policy desires to the monetary authority. Most communications are not recorded. Therefore, direct documentation is impossible (Havrilesky 1995, Ch. 2). To circumvent these obstacles the index of signaling from Administration to the Federal Reserve (SAFER) is based on the premise that the Administration’s monetary policy intentions are systematically captured in the financial press over time. The index is constructed on the assumption that the financial press efficiently extracts and reports with little bias the content of exchanges between the Administration and Federal Reserve officials. Specifically, it is assumed that the policy content of formal 3 Hakes (1990) finds a break from Martin to Burns and from Burns to Volcker. It is less clear that there are changes in operating procedures from Volcker to Greenspan. Below we present estimates for Greenspan and Volcker separately. We also estimated reaction functions combining the two regimes. 4 It is customary in reaction function studies using the federal funds rate as dependent variable to use the nominal rate, as is done in this study. If the real rate, measured as the nominal rate minus the expected (forecasted) inflation rate, is the dependent variable, adding the expected inflation rate to both sides leaves the nominal funds rate as the dependent variable and the expected inflation rate on the right side of the reaction function—the customary form of the reaction function and the one used here. 5 Havrilesky (1995) focused on the weekly federal funds rate in examining the effects of political/special interest pressures on U.S. monetary policy. This precluded the use of statevariables such as unemployment, the rate of inflation, and GNP as additional explanatory factors since these are not available weekly. Here we focus on monthly data so that we are able to include and control for the effects of these variables. Havrilesky (1995) also examined different issues which dictated the examination of different subperiods than those studied here. 6 The Federal funds rate is often employed as a measure of monetary policy action and a survey of the evidence suggests that it is probably the best measure. See Havrilesky (1995, ch. 6).
473
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud and informal communications from the Administration to the central bank is extracted by the press from speeches, news conferences, press releases, interviews, leaks, etc., and is consistently and reliably reported in the press. It is not assumed that Fed officials must read the financial press to discern Administration desires for monetary policy, but rather that press accounts are a proxy for both formal and informal communications. The SAFER index is based on articles from the Wall Street Journal for the period from January 1952 to November 1991, which mention views of the Administration on monetary policy, whether they emanate from a particular department (e.g., Treasury), official, or spokesperson, or from unidentified Administration sources. Each article was categorized as to whether it signaled a desire for monetary ease, counted as Ⳮ1, monetary tightness, counted as ⳮ1, or neither, counted as zero. The monthly SAFER index used here consists of the simple sum of pluses and minuses for each month.7
2. The Reaction Function The reaction functions estimated in this study are of the form f ⳱ AX* Ⳮ BP Ⳮ cfⳮ1 Ⳮ e ,
(1)
where f is a vector of observed values of the Federal funds rate, X is a matrix of forecasted state variables which the Fed “targets,” P is a matrix of measures of political pressures, and e is a vector of disturbances. Monetary policy is made under conditions of imperfect information. In particular the Fed’s setting of the Federal funds rate depends on forecasts of the current and future levels of state variables, which the monetary authority wishes to affect, or target. Two different approaches are taken here to measure these forecasts of the state variables which condition monetary actions: first, forecasting equations are estimated and used to construct consistent forecasts; second, as an alternative, we use the Green Book forecasts prepared by the Fed staff and presented at each FOMC meeting. We esti7 Research assistants were each assigned two- to four-year periods for scanning microfiche copies of each Wall Street Journal in those years, using the Journal’s “What’s News” digest as a guide and xeroxing and reading all columns and editorials that mentioned the Administration’s views on monetary policy, assigning a Ⳮ1, ⳮ1, or zero to each. Another research assistant independently made another pass through several months of the same microfiche copies in each year and also selected and categorized qualifying articles. The disparity between any two research assistants’ findings were negligible. Finally, Havrilesky independently read and evaluated each article on three, widely separated occasions resulting in the final form of the SAFER index. See Havrilesky (1995, ch. 2) for further details.
474
The Asymmetric Effects of Political Pressures mate reaction functions for both sets of forecasts as a check on the robustness of our findings. The first approach assumes that policymakers forecast “consistently,” which means they employ information efficiently—that is, observations used in forecasting affect the forecast of the variable in the same manner they affect the actual value of the variable. So forecasts take the form X* ⳱ E(X|Xⳮ1) ,
(2)
where Xⳮ1 is the available information set used in forecasting X. The specification of Xⳮ1 employed here consists of lagged values of a number of the major predetermined variables, which almost any macroeconomic model would specify as determinants of the state or target variables, as well as lagged values of the target variables. The predetermined variables used in the forecasting equations are the Federal funds rate, the monetary base, the industrial production index, Moody’s tripleⳮA bond rate, and lagged values of the target variables, the unemployment and inflation rates, all taken from the Citibase tape. The forecasting equations were estimated by regressing the actual values of target variables, using alternatively one, two, and three period lags, on all independent variables. From among these the forecasting equations actually used to generate X* were chosen on the basis of the standard error of regression and the Akaike information criterion. Forecasting equations were estimated using data from the whole period. Alternatively they were estimated using only data from the various subperiods studied and then used to generate X* for the subperiods from which they were estimated. The rationale for this alternative is that the forecast measures relevant for the reaction function are those of the policymakers who know the policy regime (they are its authors). It is therefore credible, and consistent with Hakes’ (1990) findings noted above, that these forecasts are best approximated by forecast equations estimated over each particular regime. A possible concern with using a forecast equation whose coefficients are estimated for a whole regime is that it imputes to the policymaker too much information—the forecast equation is estimated from data that policymakers did not have at the time the forecast was actually made. Were this the case the likely effect would be to bias our results against finding significance for the SAFER index. This follows because, to the extent policymakers are influenced by administration signaling, this effect may be reflected in the future data (such as the monetary base) used to estimate the forecast equation over an entire regime. When such forecasts are used in the reaction function they may already reflect the influence of signaling and therefore mitigate against rev475
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud elation of a significant effect of the SAFER index appearing separately in the reaction function. Given that derivation of the reaction function in a multiperiod framework would have policymakers forecasting target variables for each of several future months and reacting to these forecasts, we assume they respond to the forecast of the average value over these months and accordingly generate three-month average forecasts. We compare the reaction function estimates using these results with those using only one-month forecasts and conclude the three-month forecasts seem the more correct specification. (Both sets of results are compared below.) There are several possible shortcomings of the forecast equation procedure. Regarding the separate forecast equations estimated for each regime, one might not be convinced that FOMC members “know” the regime in the relevant sense of the term. They do not adopt regimes formally, except for the monetarist experiment and its end. Regime changes may evolve more gradually without easily identified, abrupt beginning and ending points such as are implied by the estimated regime-specific forecasting equations. The possibility that the estimated forecast equations embody significant (future) information not in fact possessed by policymakers is another shortcoming, already noted. Another concern is the rigid assumption that policymakers always make a three month ahead forecast (or alternatively, one month ahead). The impression given by the FOMC minutes is that the forecast horizon is vague and variable. Our second approach uses the Green Book forecasts of the state variables in the reaction function. We did this using the one quarter ahead forecasts and, alternatively, using the average of the one-quarter-ahead and the two-quarter-ahead Green Book forecasts.8 One shortcoming of this approach is that the one-quarter-ahead forecasts are only available from 1965 through 1989, and the two-quarter-ahead forecasts only from 1970 through 1987. The forecasting equation approach allowed us to estimate and examine reaction functions over a longer period. Another possible shortcoming of using the Green Book forecasts is that FOMC members may rely to varying degrees on other forecasts—Fed Bank presidents have forecasts from their own staffs, for example. This is a consideration that may favor the forecasting equation approach which assumes that FOMC members, like other economic agents, form rational expectations based on all available information. 8
Green Book forecasts are as of a given FOMC meeting date. A monthly series was created as a weighted average of meeting date forecasts. If, for example, the FOMC met on (11/15/75) and (12/15/75), the (12/75) observation would be 0.5 times each meeting date’s forecast. The assumption implicit in this calculation is that the forecast that conditioned the FOMC directive to the Open Market Desk continued to be that from the previous meeting until a subsequent meeting was held.
476
The Asymmetric Effects of Political Pressures 3. Estimates of Executive Branch Pressures on Monetary Policy Here we report the results obtained estimating reaction functions measuring executive branch desires via the SAFER index S.9 The state variables which the monetary authority is assumed to target are the unemployment rate and the rate of inflation in the consumer price index. Forecasts of the state variables are measured in two ways as described in the previous section: from estimated forecast equations and by Green Book forecasts. When we consider the Green Book forecasts we add a third state variable, real GNP growth. We begin by considering reaction function estimates using the fitted values from forecast equations. Reaction Function Estimates with Fitted Values from Forecast Equations The specific forms of (1) estimated here are ˙ t Ⳮ ␣3St Ⳮ ␣4 ftⳮ1 Ⳮ et ft ⳱ ␣0 Ⳮ ␣1U* t Ⳮ ␣2P*
(3)
˙ t Ⳮ ␣5SEt Ⳮ ␣6STt Ⳮ ␣4 ftⳮ1 Ⳮ et , ft ⳱ ␣0 Ⳮ ␣1U* t Ⳮ ␣2P*
(4)
and
˙ t are the forecasts of the unemployment and inflation rates where U*t and P* respectively, St is the value of the SAFER index in period t, and SEt and STt are SAFER signals for ease and tightness, respectively. It should be emphasized that a monetary aggregate enters these reaction functions since the monetary base is one of the variables in the forecasting equations for ˙ . Similarly the industrial production index and the triple-A bond U* t and P* t rate enter the reaction functions in the same way. The basic premise of our procedure however is that these variables enter in a way that reflects their influence on the inflation and unemployment targets.10 Table 1 shows the reaction function estimates when forecasts of the state variables are fitted values from forecast equations. The time period over which (3) and (4) are estimated is 1959:5 through 1991:10. The starting point precedes 1961, the year when executive branch attempts to influence 9
Havrilesky (1995) also reports results using a “threat-augmented SAFER” index which is a combined measure of executive and congressional pressures on the Federal Reserve. In an earlier version of the paper we found that results using SAFER and the threat-augmented SAFER differed only slightly. Therefore, here we confine ourselves to analysis of the effects of executive branch signaling. 10 Residuals from initial OLS estimates were tested for autocorrelation using the Durbin hstatistic. Since there was evidence of autocorrelation for the period as a whole and almost all subperiods, the regressions were reestimated using Hatanaka’s two-step procedure.
477
478
TABLE 1. Reaction Function Estimates with Fitted Values of Forecast Equations U*
P˙*
S
0.303 (1.90)
ⳮ0.004 (ⳮ0.156)
80.44 (4.37)
ⳮ0.103 (ⳮ3.79)
0.304 (1.60)
ⳮ0.007 (ⳮ0.210)
84.66 (4.08)
0.380 (1.34)
ⳮ0.050 (ⳮ1.22)
51.76 (2.19)
0.472 (1.68)
ⳮ0.066 (ⳮ1.62)
41.63 (1.77)
1.85 (3.16)
ⳮ0.200 (ⳮ2.83)
124.35 (3.66)
1.82 (3.12)
ⳮ0.192 (ⳮ2.76)
123.0 (3.65)
Time Period
Const
59:5–91:10
59:5–70:1
70:2–79:9
SE
ⳮ0.057 (ⳮ1.58)
ST
ⳮ0.159 (ⳮ3.49)
ⳮ0.062 (ⳮ1.58) 0.025 (0.471)
ⳮ0.167 (ⳮ2.97)
ⳮ0.012 (ⳮ0.318) 0.030 (0.681)
ⳮ0.105 (ⳮ1.56)
ftⳮ1
q/R¯2
DW
0.919 (56.73)
0.60 0.95
2.23
0.913 (47.42)
0.35 0.96
1.90
0.950 (36.55)
ⳮ0.01 0.97
2.04
0.943 (36.71)
0.04 0.97
2.11
0.813 (17.25)
0.50 0.96
1.92
0.808 (17.23)
0.50 0.96
1.93
79:10–82:6
79:8–87:8
87:9–91:10
ⳮ2.61 (ⳮ0.198)
0.169 (0.186)
149.8 (0.723)
ⳮ2.31 (ⳮ0.162)
0.187 (0.174)
123.71 (0.549)
0.783 (0.709)
0.090 (0.631)
200.86 (2.95)
0.779 (0.664)
0.096 (0.634)
203.12 (2.86)
3.81 (2.51)
ⳮ0.454 (ⳮ2.75)
24.71 (0.803)
4.02 (2.66)
ⳮ0.474 (ⳮ2.88)
22.17 (0.720)
ⳮ0.280 (ⳮ1.59) ⳮ0.631 (ⳮ1.92)
ⳮ0.130 (ⳮ0.667)
ⳮ0.167 (ⳮ2.48) ⳮ0.187 (ⳮ1.85)
ⳮ0.140 (ⳮ1.42)
ⳮ0.032 (ⳮ1.34) ⳮ0.034 (ⳮ1.40)
0.056 (0.567)
0.998 (1.96)
0.29 0.63
1.81
0.995 (1.82)
0.26 0.61
1.60
0.772 (11.34)
0.44 0.90
1.95
0.776 (10.82)
0.36 0.90
1.79
0.829 (10.20)
0.46 0.97
1.94
0.820 (10.15)
0.44 0.97
1.96
479
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud monetary policy began in earnest (see Havrilesky 1995, ch. 2).11 The results for the whole period reveal the estimated coefficient for the inflation rate to be statistically significant while the estimated coefficient for the unemployment rate is not. The estimated coefficient for the SAFER index is also significant and has the correct sign. Estimation of (4) indicates that tightness signaling drives this significance, though the index of signals for ease has a marginal significance level of 0.11. The estimates for the Martin chairmanship, 1959:4–1970:1, closely resemble those for the whole period. The estimated coefficient for the inflation rate is statistically significant while the coefficient for the unemployment rate is not. In addition, the estimated coefficient for tightness signals is statistically significant, but the coefficient for ease signals is not. Estimation of (3) and (4) for the Burns-Miller era, 1970:2–1979:9, show the estimated coefficient for both the unemployment rate and the inflation rate variables to be statistically significant.12 The SAFER index is not significant for this subperiod either when entered directly or when broken into ease and tightness signals. The measure of tightness does, however, have a marginal significance level of 0.12. The results for the Volcker chairmanship, 1979:10–1987:8, show a significant coefficient for the inflation rate but not for the unemployment rate. The SAFER index is statistically significant and during this period it appears that the response was more to ease signals (marginal significance 0.07) than to those for tightness (0.16). During the Greenspan period, 1987:9–1991:10, the unemployment rate is significant but the inflation rate is not. The SAFER index is not significant, but the marginal significance level of the index of tightness signals (0.16) is greater than that for ease signals. Finally, because of its much publicized uniqueness, (3) and (4) were estimated for that subperiod of the Volcker regime corresponding to the socalled “monetarist experiment,” 1979:10–1982:6, when the Federal Reserve targeted on the money supply rather than the Federal funds rate.13 (Recall 11
There was no executive branch signaling in 1959 and 1960. In fact, the 1950s were in general a period of very little signaling, reflective of Eisenhower’s hands-off attitude toward monetary policy. Signaling began in earnest with the advent of the Kennedy Administration in 1961 and, with the exception of the Ford interregnum, escalated to considerably higher levels during the 1970s and 1980s. 12 The tenure of G. William Miller as chairman of the Fed extended from March 1978 through July 1979. This was deemed too short a period to be treated separately. The first months of Volcker’s term, prior to the October shift in policy procedures, were also considered as part of the 1970s regime. 13 The appropriate endpoint for the monetarist experiment is unclear. We have estimated reaction functions for this subperiod with the alternative endpoint 1982:9. This change did not result in a significant change in our results. With either end point, however, it should be noted
480
The Asymmetric Effects of Political Pressures that the monetary base enters the reaction function via the forecasting equa˙ ). Perhaps because of the paucity of data for this subtions for U*t and P* t period, estimated coefficients for neither the unemployment nor the inflation rate are statistically significant. The estimated coefficient for the SAFER index is not significant in specification (3). When SAFER is separated into ease and tightness signals in specification (4), the measure of ease signals is significant at the 10% level consistent with the estimate for the whole Volcker period. The equations used to generate the forecasts employed in Table 1 were estimated from data for the whole period covered by our study. The results in Table 1, however, indicate that there were quite different reaction functions during different Federal Reserve chairmanships, a result also found by Hakes (1990). If there were different policy regimes, then there may have been shifts in the coefficients of the reduced form equations that characterize the economy—the gist of the Lucas critique. To check for this possibility and its effect on the reaction functions estimated for the subperiods reported in Table 1, we estimated separate forecasting equations for each subperiod using only data for that subperiod. We then re-estimated the reaction functions for each subperiod using fore˙ generated by the forecasting equation estimated specific casts of U*t and P* t to that subperiod. These results are shown in Table 2. Comparing them with those in Table 1 reveals no significant differences. As another check of the sensitivity of our results to the specification of the forecast equation, we also estimated forecast equations using one month ahead forecasts of U*t and ˙ . Reaction function estimates using these forecasts did not differ signifiP* t cantly from those in Tables 1 and 2. Reaction Functions Estimated with Green Book Forecasts Tables 3–5 show reaction function estimated with the state of the economy variables measured by Green Book forecasts. The estimates in Table 3 parallel those in Table 1 except for the shortened time periods due to lack of availability of Green Book forecasts for some years. Table 4 shows estimates with an additional state variable, the rate of real GNP growth (Y˙*). Table 5 tests the effect of extending the forecast horizon from 3 to 6 months. Comparing the results in Table 3 with those in Table 1, there are a number of differences in the estimated coefficients on the state variables, but little difference in the estimated effects of the signaling indices. With the exception of the Burns chairmanship, the pattern of coefficients on the state variables is similar in the two tables but in a number of that there are not many observations for this subperiod, and as a consequence our estimates will be imprecise.
481
TABLE 2. Reaction Function Estimates: Regime Specific Forecast Equations 482
Time Period 59:5–70:1
70:2–79:9
79:10–82:6
79:8–87:8
87:9–91:10
U*
P˙*
S
0.400 (1.35)
ⳮ0.052 (ⳮ1.19)
52.82 (1.74)
ⳮ0.062 (ⳮ1.57)
0.488 (1.67)
ⳮ0.067 (ⳮ1.57)
41.43 (1.39)
1.95 (3.59)
ⳮ0.215 (ⳮ3.33)
107.96 (2.36)
1.92 (3.55)
ⳮ0.211 (ⳮ3.26)
108.14 (2.38)
ⳮ22.22 (ⳮ1.48)
1.21 (1.14)
218.0 (1.06)
ⳮ11.52 (ⳮ0.772)
0.768 (0.639)
142.76 (0.645)
0.557 (0.494)
0.108 (0.748)
217.28 (3.43)
0.557 (0.467)
0.111 (0.720)
217.02 (3.31)
Const
3.84 (2.29)
ⳮ4.49 (ⳮ2.49)
25.66 (0.717)
4.03 (2.40)
ⳮ0.468 (ⳮ2.60)
22.94 (0.64)
SE
0.028 (0.522)
ST
ⳮ0.169 (ⳮ3.01)
ⳮ0.032 (ⳮ0.886) 0.001 (0.026)
ⳮ1.04 (ⳮ1.55)
ⳮ0.339 (ⳮ2.03) ⳮ0.728 (ⳮ2.27)
ⳮ0.100 (ⳮ0.547)
ⳮ0.166 (ⳮ2.52) ⳮ0.183 (ⳮ1.84)
ⳮ0.144 (ⳮ1.49)
ⳮ0.036 (ⳮ1.49) ⳮ0.038 (ⳮ1.57)
0.052 (0.532)
ftⳮ1
q/R¯2
DW
0.948 (33.3)
ⳮ0.01 0.97
2.01
0.942 (33.90)
0.04 0.97
2.07
0.829 (15.47)
0.48 0.96
1.96
0.824 (15.38)
0.97 0.96
1.96
1.72 (3.23)
0.24 0.59
1.20
1.29 (2.64)
0.27 0.54
1.38
0.775 (11.67)
0.38 0.91
1.85
0.776 (11.22)
0.30 0.91
1.71
0.823 (9.22)
0.42 0.97
1.89
0.815 (9.15)
0.39 0.97
1.92
The Asymmetric Effects of Political Pressures instances the Green Book forecasts have lower levels of significance. For example, the whole period estimate for the inflation variable is significant at the 1% level in Table 1, but only at the 10% level in Table 3. For the Burns chairmanship, neither the Green Book forecast of inflation or unemployment is significant in Table 3, while forecasts of both are significant in Table 1. With respect to the SAFER indices, for the whole period in Table 3 both measures of ease and tightness signals are significant at the 5% level, while only tightness signals were significant in Table 1. For the Martin period the coefficient on the SAFER index is significant while it was not in Table 1. Still, when SAFER is split into ease and tightness signals the pattern is the same in both Tables—only the index of tightness signals is significant. Otherwise, the pattern of estimated coefficients on the signaling indices does not differ significantly between the two tables. When we add real GNP growth (Y˙*) as an additional state variable in Table 4, it enters significantly for the whole period and for several of the subperiods. Inclusion of this additional variable does not, however, result in significant changes (relative to the estimates in Table 3) in the estimated pattern of responses to the signaling indices except during the period of the monetarist experiment. During that period the SAFER index is now significant only at the 10% level, and when SAFER is broken into ease and tightness signals neither measure is significant. Finally, Table 5 shows estimates where the Green Book forecasts are for a 6-month horizon. Given available data for these forecasts, estimates are for only three of the subperiods. A comparison with Table 4 (which is the relevant table because Y˙* is included in the estimates in Table 5) reveals that this change in forecast horizon has no dramatic effect on our estimates.14,15 14
In an earlier version of the paper, we also addressed the robustness issue by estimating equations that varied the specification of the state of the economy variables. One specification added first differences of the state variables. Another, a variant of the Ramsey (1969) specification test, added squared values of the state variables. Estimated coefficients on our indexes of executive branch signaling were not sensitive to these changes in specification. Our tests for non-linearity (the Ramsey tests) produced the same results as Hakes and Gamber (1992). 15 We have considered the possibility that SAFER is responding to the Federal funds rate, rather than the other way around. For example, if the Administration were to respond to a rise (fall) in the Federal funds rate by signaling for ease (tightness), then the Federal funds rate and SAFER would be positively correlated, and one would expect the signs on S in Tables 1–5 to be positive. The coefficients on SAFER in the tables are, however, almost always negative. All the significant coefficients are negative, as would be predicted if causality were to run only from SAFER to the Federal funds rate. However, what if at times the Federal funds rate responds to SAFER, while at other times SAFER responds to the rate? Then in the former case the sign would be negative and in the latter, positive, and on balance the estimated coefficients on SAFER might appear insignificant. We cannot rule out this possibility. Thus, while possible
483
484
TABLE 3. Reaction Function Estimates: Green Book Forecasts Time Period 66:5–89:12
66:5–70:1
70:2–79:9
Const
U*
P˙*
0.439 (1.41)
0.015 (0.328)
0.069 (1.86)
0.443 (1.43)
0.011 (0.232)
0.067 (1.81)
1.67 (1.50)
ⳮ0.456 (ⳮ1.70)
0.117 (2.36)
1.70 (1.53)
ⳮ0.448 (ⳮ1.67)
0.117 (2.37)
0.682 (0.956) 0.735 (1.05)
ⳮ0.029 (ⳮ0.392) ⳮ0.038 (ⳮ0.522)
0.061 (1.13) 0.059 (1.12)
S
SE
ftⳮ1
q/R¯2
DW
0.893 (25.21)
0.46 0.93
1.90
0.895 (24.92)
0.48 0.92
1.95
0.942 (32.78)
0.20 0.98
2.05
ⳮ0.300 (ⳮ3.89)
0.925 (29.84)
0.17 0.98
2.00
0.51 0.95 0.51 0.96
1.86
ⳮ0.097 (ⳮ1.39)
0.876 (10.46) 0.736 (1.05)
ST
ⳮ0.120 (ⳮ3.66) ⳮ0.098 (ⳮ2.24)
ⳮ0.153 (ⳮ2.86)
ⳮ0.212 (ⳮ3.67) ⳮ0.097 (ⳮ1.08) 0.002 (0.042) 0.044 (0.995)
1.89
79:10–82:6
79:8–87:8
87:9–89:12
ⳮ19.50 (ⳮ1.70)
ⳮ0.576 (ⳮ0.892)
0.020 (0.084)
ⳮ0.897 (ⳮ0.083)
ⳮ0.379 (ⳮ0.499)
0.032 (0.118)
1.25 (1.70)
ⳮ0.071 (ⳮ0.387)
0.131 (0.995)
1.34 (0.802)
ⳮ0.058 (ⳮ0.314)
0.147 (1.12)
12.09 (3.16)
ⳮ1.60 (ⳮ3.01)
0.002 (0.027)
11.97 (2.87)
ⳮ1.58 (ⳮ2.70)
ⳮ0.004 (ⳮ0.048)
ⳮ0.386 (ⳮ2.38) ⳮ0.685 (ⳮ2.07)
ⳮ0.113 (ⳮ0.608)
ⳮ0.156 (ⳮ2.43) ⳮ0.215 (ⳮ2.27)
ⳮ0.097 (ⳮ1.05)
ⳮ0.037 (ⳮ1.33) ⳮ0.038 (ⳮ1.35)
ⳮ0.039 (ⳮ0.386)
2.61 (3.69)
0.51 0.18
1.33
1.25 (2.54)
0.53 0.13
1.79
0.864 (6.07)
0.48 0.80
1.94
0.845 (5.91)
0.49 0.79
1.89
0.601 (4.32)
0.59 0.93
1.93
0.607 (3.94)
0.55 0.94
1.85
485
486
TABLE 4. Reaction Function Estimates: Green Book Forecast Including Y* Time Period
Const
66:5–89:12
0.193 (0.597)
0.010 (0.229)
0.197 (0.611)
0.059 (0.134)
66:5–70:1
70:2–79:9
U*
P˙*
Y*
S
0.076 (2.12)
0.037 (2.01)
ⳮ0.122 (ⳮ3.74)
0.074 (2.08)
0.037 (2.01)
1.28 (1.09)
ⳮ0.039 (ⳮ1.41)
0.096 (1.76)
0.025 (0.869)
1.32 (1.13)
ⳮ0.381 (ⳮ0.139)
0.098 (1.80)
0.025 (0.833)
0.285 (0.422)
ⳮ0.026 (ⳮ0.369)
0.054 (1.04)
0.024 (1.61)
0.372 (0.544)
ⳮ0.031 (ⳮ0.448)
0.053 (1.03)
0.021 (1.43)
SE
ⳮ0.101 (ⳮ2.32)
ST
ⳮ0.153 (ⳮ2.86)
ⳮ0.219 (ⳮ3.69) ⳮ0.104 (ⳮ1.12)
ⳮ0.307 (ⳮ3.89)
ⳮ0.002 (ⳮ0.045) 0.039 (0.839)
ⳮ0.092 (ⳮ1.31)
ftⳮ1
q/R¯2
DW
0.910 (26.51)
0.42 0.93
1.89
0.912 (26.46)
0.43 0.93
1.93
0.963 (27.02)
0.19 0.98
2.08
0.944 (25.08)
0.15 0.98
2.01
0.926 (13.00)
0.41 0.95
1.78
0.915 (12.83)
0.41 0.96
1.76
79:10–82:6
79:8–87:8
87:9–89:12
9.94 (2.14)
ⳮ0.130 (ⳮ0.356)
ⳮ0.182 (ⳮ1.06)
0.387 (3.76)
10.01 (1.98)
ⳮ0.115 (ⳮ0.292)
ⳮ0.178 (ⳮ0.978)
0.378 (3.58)
0.944 (6.14)
ⳮ0.062 (ⳮ0.365)
0.208 (1.74)
0.107 (2.06)
1.03 (0.670)
ⳮ0.048 (ⳮ0.281)
0.228 (1.92)
0.108 (2.07)
7.71 (3.04)
ⳮ1.03 (ⳮ3.00)
0.085 (1.21)
0.147 (3.02)
8.01 (2.74)
ⳮ1.07 (ⳮ2.66)
0.086 (1.16)
0.151 (2.97)
ⳮ0.302 (ⳮ1.81) ⳮ0.504 (ⳮ1.59)
ⳮ0.194 (ⳮ0.950)
ⳮ0.164 (ⳮ2.58) ⳮ0.227 (ⳮ2.40)
ⳮ0.103 (ⳮ1.11)
ⳮ0.026 (ⳮ0.881) ⳮ0.026 (ⳮ0.867)
ⳮ0.060 (ⳮ0.564)
0.511 (3.60)
0.09 0.74
2.09
0.507 (3.33)
0.12 0.74
1.96
0.827 (6.90)
0.32 0.91
1.78
0.805 (6.79)
0.33 0.91
1.72
0.668 (7.82)
0.31 0.97
2.09
0.660 (6.89)
0.28 0.96
2.03
487
488
TABLE 5. Reaction Function Estimates: Green Book Forecasts, Two-Quarter Forecast Horizon Time Period
Const
U*
P˙*
Y*
S
70:2–79:9
0.204 (0.231)
ⳮ0.057 (ⳮ0.602)
0.085 (1.10)
0.043 (1.59)
ⳮ0.001 (ⳮ0.018)
0.303 (0.339)
ⳮ0.059 (ⳮ0.618)
0.088 (1.12)
0.038 (1.39)
12.04 (2.33)
ⳮ0.512 (ⳮ1.36)
0.045 (0.219)
0.635 (4.45)
12.18 (2.25)
ⳮ0.490 (ⳮ1.24)
0.053 (0.248)
0.646 (4.42)
0.652 (0.628)
ⳮ0.055 (ⳮ0.467)
0.513 (4.25)
0.237 (3.34)
0.654 (0.631)
ⳮ0.045 (ⳮ0.380)
0.525 (4.38)
0.240 (3.37)
79:10–82:6
79:8–87:8
SE
0.039 (0.854)
ST
ⳮ0.090 (ⳮ1.28)
ⳮ0.301 (ⳮ1.96) ⳮ0.535 (ⳮ1.86)
ⳮ0.175 (ⳮ0.890)
ⳮ0.193 (ⳮ3.05) ⳮ0.238 (ⳮ2.46)
ⳮ0.150 (ⳮ1.57)
ftⳮ1
q/R¯2
DW
0.930 (10.15)
0.45 0.95
1.91
0.913 (9.93)
0.44 0.95
1.86
0.457 (3.50)
0.06 0.77
2.07
0.445 (3.31)
0.11 0.78
2.02
0.674 (7.86)
0.36 0.92
1.84
0.665 (7.85)
0.37 0.91
1.81
The Asymmetric Effects of Political Pressures 4. Fed Response to Administration Pressures It was noted in the introduction that conventional wisdom among political commentators and economists generally seems to hold that political administrations tend to have a bias for easier monetary policy. Indeed this impression seems consistent with the fact that over the whole period studied here, 1959:4–91:10, there were 207 ease signals and 134 for tightness. There is considerable variation across regimes in the preponderance of signal type however, with tightness signals sometimes dominant. The question is, Whatever the administration bias in a particular regime, how has the Fed responded to it? For the whole period 1959–1991, the results in Table 1 using forecast equations suggest that the Fed was more responsive to tightness signals. For the shorter whole period 1965–1989 the results in Tables 3 and 4 using Green Book forecasts suggest the same, though less strongly—the tightness coefficient estimates are roughly 50% larger than the ease coefficients in the latter two tables, about 150% larger in Table 1. Our finding of a stronger Fed response to tightness signals over the whole period does not mean the Fed generally has a preference for more ease relative to the administration, or put the other way, that the administration generally has a preference for more tightness relative to the Fed. Relative preferences are revealed by what signals are sent. At those times when an administration signals for ease it prefers more ease than the Fed, and at those times when it signals for tightness it prefers more tightness than the Fed. Our findings only indicate that over the whole period the Fed was more responsive to tightness signals. This however masks considerable differences across regimes. During the Martin regime, 1959:4–70:1, the estimates in Tables 1 and 2 indicate a significant Fed response to tightness signals but not to ease. The portion of the Martin years, 1965:5–70:1, we were able to examine using Green Book forecasts. Tables 3 and 4 indicate the same thing. Over the whole Martin regime there were 25 ease signals and 23 tightness signals, while for the 1965:5–70:1 portion there were 6 ease versus 11 tightness signals. For the Martin regime administration pressures were relatively balanced between ease and tightness. Since the Fed seems to have responded only to administration signals for tightness, on balance this suggests that during the Martin years monetary policy was tighter than it would have been in the absence of pressure from the executive branch. For the Burns regime, 1970:2–79:9, none of the results in Tables 1–5 reverse causation would not lead us to incorrectly conclude that SAFER was a significant influence on the Federal funds rate, it could result in our not finding a significant effect for SAFER when in fact there was one. The Ramsey (1969) specification tests referred to in the previous footnote, however, did not indicate the presence of such reverse causation.
489
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud indicate a statistically significant response to signaling, though Tables 1 and 2 weakly suggest some responsiveness to tightness signals. During the Burns years there were 57 ease signals versus 24 tightness signals, but apparently with little effect on monetary policy. Over this regime administration preferences do seem biased toward ease. During the monetarist experiment years, 1979:10–82:6, of the Volcker regime the results in Tables 1–5 all suggest that the Fed only responded to ease signals. This despite the fact there were many more tightness signals, 30, than ease signals, 15, suggesting that on balance administration preferences tilted towards tightness over this period. For the whole Volcker regime, 1979:8–87:8, the results in Tables 1–5 indicate the same pattern of response. There were 67 ease signals and 54 tightness signals during this period, indicating a slight tilt of administration preference towards ease overall. The Fed’s response suggests that monetary policy was easier than might otherwise have been the case in the absence of administration pressure, both during the monetarist experiment years and over the whole Volcker regime. Nonetheless, the significant response of the Fed funds rate to inflation during this period indicates inflation fighting was a priority. The significant response to ease signals, however, suggests Fed policy might have been even more anti-inflationary but for administration pressure. The Fed does not appear to have responded to signaling during the early Greenspan years, 1987:9–91:10, Tables 1 and 2, though the response to ease signaling in Table 2 shows some weak significance. Administration preferences were very skewed towards ease—43 ease versus 3 tightness signals during the period. The same story holds for the Green Book forecast results, Tables 3 and 4, for the shorter period 1987:9–89:12. A final question we consider is that of the quantitative effect of administration signaling. How large is the effect of signaling on the federal funds rate? Clearly, this depends on the amount of signaling and the effect per signal as estimated by coefficients on SAFER. Table 6 provides some illustrative evidence on the quantitative importance of signaling for time periods where there was statistically significant evidence of an effect. For each month (during these intervals) when there were two or more signals the table shows the effect on the federal funds rate measured in percentage points. For other months where there was one signal, the estimated effect is one half the smallest number in the table for that period. The numbers in columns (1) and (3) are calculated using estimated coefficients from Tables 1 and 3, respectively. For the monetarist subperiod (1979:10–1982:6) the table gives two sets of estimates—one based on the coefficients from the reaction function estimated for that subperiod and one based on the coefficient from the estimate for the whole Volcker period. 490
TABLE 6. Impact Effect of Administration Signaling
Martin Period 07/63 02/65 02/69 05/69
Volcker (1979:10–1982:6) 04/80 05/80 10/80 10/81
Volcker (1979:8–1987:8) 04/80 05/80 10/80 10/81 08/82 10/82 11/82 01/83 11/83 12/83 02/84 05/84 11/84 12/84 02/85
(1)
(3)
0.334 0.334 0.334 0.334
0.600 0.600 0.600 0.600
(1)
(3)
ⳮ1.262 ⳮ1.262 ⳮ1.262 ⳮ1.893
ⳮ1.370 ⳮ1.370 ⳮ1.370 ⳮ2.055
(1)
(3)
ⳮ0.374 ⳮ0.374 ⳮ0.374 ⳮ0.561 ⳮ0.374 ⳮ0.748 ⳮ0.374 ⳮ0.374 ⳮ0.561 ⳮ0.561 ⳮ0.748 ⳮ0.748 ⳮ0.561 ⳮ0.374 ⳮ0.374
ⳮ0.430 ⳮ0.430 ⳮ0.430 ⳮ0.645 ⳮ0.645 ⳮ0.860 ⳮ0.430 ⳮ0.430 ⳮ0.645 ⳮ0.645 ⳮ0.860 ⳮ0.860 ⳮ0.645 ⳮ0.430 ⳮ0.430
NOTE: The numbers on the table give the impact effect of administration signaling on the Federal funds rate measured in percentage points for months where there were 2 or more signals. Results are shown only where there was statistically significant evidence of the effects of signaling. The column labeled (1) uses the coefficient estimate on the relevant SAFER measure from Table 1. The column labeled (3) uses the coefficient from Table 3.
491
Richard T. Froyen, Thomas Havrilesky, and Roger N. Waud One point to note is that the numbers in the table give only the impact effects of signaling. Since our reaction functions are based on a partial adjustment mechanism (they include a lagged dependent variable), the effects of signals persist over time. Thus, in periods, such as late 1982 and early 1983, when signals “bunch up,” the effects in some months will be much larger than the numbers shown in the table. Overall, the estimated effect of signaling, as illustrated by the numbers in Table 6, suggest that administration pressures on the Fed have from time to time had a substantial impact on the Fed funds rate. 5. Conclusion We began with the question of whether political pressures add significant explanatory power in reaction functions which include economic state variables and, if so, what effect such pressures had on the stance of monetary policy. Our estimates for the whole period indicate that our measure of political pressures (SAFER) does have significant explanatory power. These estimates also indicate that the effect on monetary policy of executive branch signaling has been to produce a tighter policy stance, even though administration signals for ease were more frequent than for tightness. Our estimates for separate Federal Reserve chairmans’ tenure, however, show considerable variation in the significance of our measure of political pressures and of the direction of its effect on the ease or tightness of monetary policy. Our measure of political pressures is statistically significant in the Martin and Volcker chairmanships. In the former case signaling appears to have resulted in a tighter monetary policy stance, while in the latter case it had the opposite effect. Evidence for any effect of signaling is weak for the Burns and Greenspan periods. What evidence there is suggests signaling tilted policy toward tightness in the Burns period and ease in the Greenspan period. The hypothesis that signaling had no effect in these periods cannot, however, be rejected at conventional levels of statistical significance. However, taken together, the weak evidence for signaling in the Burns and Greenspan periods, and the strong evidence for the Martin and Volcker chairmanships, indicate that signaling resulted in a tighter monetary policy stance pre-1980, while post-1980 it tilted policy toward ease. Received: November 1994 Final version: May 1996
References Hakes, David. “The Objectives and Priorities of Monetary Policy under Different Federal Reserve Chairman.” Journal of Money, Credit, and Banking 22 (August 1990): 327–37. 492
The Asymmetric Effects of Political Pressures Hakes, David, and Edward Gamber. “Does the Federal Reserve Respond to Errant Money Growth? Evidence from Three Monetary Regimes.” Journal of Money, Credit, and Banking 24 (February 1992): 127–34. Havrilesky, Thomas. The Pressures on American Monetary Policy. Boston: Kluwer Academic Publishers, 1995. Khoury, Salwa. “The Federal Reserve Reaction Function: A Specification Search.” In The Political Economy of American Monetary Policy, edited by Thomas Mayer. Cambridge: Cambridge University Press, 1990. Mayer, Thomas, ed. The Political Economy of American Monetary Policy. Cambridge: Cambridge University Press, 1990. Ramsey, James B. “Tests for Specification Errors in Classical Linear Least Squares Regression Analysis.” Journal of the Royal Statistical Society Series B 31 (1969): 350–71.
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