There is still some doubt about those economic variables that really matter for the Fed’s decisions. In comparison with other estimations, this study uses the approach of Bayesian model averaging (BMA). The estimations show that over the long-run inflation, unemployment rates and long-term interest rates are the crucial variables in explaining the Federal Funds Rate. In the other two estimation samples, also the fiscal deficit and monetary aggregates were of relevance. There is also evidence for interest rate smoothing. In addition, we account for parameter instability by combining BMA with time-varying coefficient (TVC) modelling. We find strong evidence for structural breaks. Finally, a model average is constructed via an TVC-BMA approach.
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Khoury (1993) provides an overview of studies about the Fed’s reaction function until 1986.
Given the problems with measuring the output gap (see e.g. Vera 2011), we decided not to include an output gap variable but just industrial production and productivity growth in our estimations.
The optimal monetary policy in case of an oil price shock depends on the source of the shock (Bodenstein et al. 2012).
Catao and Terrones (2005) show that the effects of fiscal deficits on inflation are most prevalent in developing countries and countries with high inflation rates. On the other hand, there is no significant relationship between government deficits and inflation in developed countries or countries with low inflation rates. However, Sims (2011) points out the role of fiscal deficits during the Great Inflation in the US.
However, newer versions of the Taylor rule also include long-term interest rates (see e.g., Casellina and Uberti 2008).
Mattesini and Becchetti (2009) argue that the effect mainly results from the fact that the Fed decreases interest rates when stocks are undervalued.
Take, for instance, this meaningful quote by Governor Lael Brainard in 2014: “Although its founding statute makes no explicit mention of financial stability, the Federal Reserve was created in response to a severe financial panic, and safeguarding financial stability is deeply ingrained in the mission and culture of the Federal Reserve Board. Today, financial stability is more important than ever to the work of the Federal Reserve Board” (Brainard 2014).
Here, especially the so-called hyper-g priors for the estimation of the regression parameters should be mentioned which are presented in Liang et al. (2008) and further developed in Feldkircher and Zeugner (2009). Feldkircher and Zeugner (2009: 4) criticise that too little attention is given to the fact that the value of \(g_r\) determines how posterior model probabilities are divided among models: if \(g_r\) is chosen to be relatively small, the posterior model mass will be concentrated on relatively few models. A relative large value of \(g_r\), in contrast, implies that posterior model probabilities are allocated relatively evenly among models. To take account of this fact, the value of \(g_r\) should be chosen dependent on the data: in noisier data sets, posterior model mass should be distributed more evenly than in less noisy data sets, where it should be clearer to determine the ‘true’ model. Hence, \(g_r\) is not chosen to be a constant any more but to be itself randomly distributed according to a certain probability distribution.
Wang and Zivot (2000) do, however, not use an averaging approach.
Of course, the estimation procedure can be adopted using, for example, an \(MC^3\) sampler to reduce computing time. This is recommended when including more explanatory variables.
The mean of our shadow rate is 14.7, whereas the mean of the EFFR is 5.4.
Goodfriend and King (2005) argue that the Fed did not conduct monetary targeting but used increases in interest rates to squeeze out inflation. Thus, it is controversial to call this period the “monetarist experiment”.
Furthermore, the PSD for industrial production is larger than the corresponding PM. Thus, the “true” coefficient could also be zero meaning that this variable does not have any impact on the Fed’s decisions in the long run. This is also evident from the low PIP of this variable. However, it is true for all variables with the exception of the most influential ones that the PSD exceeds the PM.
At least for the time of the chairmanship of Greenspan, this result is not so surprising. Vanderhart (2000) finds that CPI inflation was not a significant determinant of Federal Funds Rate changes during that time. He argues that the Fed instead tried to prevent inflation dangers at an early stage, i.e. when producer prices increased. Thus, we should not overinterpret these “wrong” signs.
An explanation for the large value of the PM of the unemployment rate between 1988 and 1992, that is in line with Gamber and Hakes (2006), could be that Greenspan seeked to be re-appointed by Bush. It is also supported by Jones and Snyder (2014) who find the Fed to behave more dovish under Republican presidents.
For instance, this question is discussed by Cogley and Sargent (2005b). A good summary of this debate is given in their paper.
Of course, we are more happy to share all results not presented here with people interested in it.
It is worth mentioning that the Fed did not target the Federal Funds Rate directly from 1951 to 1965 (Schwartz 2003) which might explain these deviations.
This can be explained by the fact that the Fed did not set explicit targets for the Federal Funds Rate after October 1979. This situation lasted until 1982 when Volcker declared that the Fed does not rely on monetary aggregates any more. Cook (1989) finds evidence that the Federal Funds Rate during the Volcker era was driven to some extent by market forces and not by the Fed’s decisions.
The Fed’s balance sheet total peaked in January 2015 (4.5 trillion US Dollar) but began to decrease afterwards.
It has to be emphasised that they do not intend to estimate the Fed’s reaction function. Still it is useful to compare our results with their results.
This is in contrast to the study of Peersman (2005) which argues that the Fed’s monetary policy was loose before the 2001 crisis and too restrictive afterwards. According to him, the relatively restrictive monetary policy was—among other factors—capable for the low output growth rates in 2001.
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The authors wish to thank Jürgen Kähler and two anonymous referees for very useful comments and suggestions. Furthermore, we are very grateful to Marco J. Lombardi and Feng Zhu for sharing their shadow rate. We would also like to thank Stefan Zeugner and the editor of the journal.
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Wölfel, K., Weber, C.S. Searching for the Fed’s reaction function. Empir Econ 52, 191–227 (2017). https://doi.org/10.1007/s00181-016-1076-6
- Monetary policy reaction functions
- Model uncertainty
- Bayesian model averaging
- Parameter instability