This paper estimates a dynamic stochastic equilibrium model in which agents use a Bayesian rule to learn about the state of monetary policy. Monetary policy follows a nominal interest rate rule that is subject to regime shifts. The following results are obtained. First, the author’s policy regime estimates are consistent with the popular view that policy was marked by a shift to a high-inflation regime in the early 1970s, which ended with Volcker’s stabilization policy at the beginning of the 1980s. Second, while Bayesian posterior odds favor the “full-information” version of the model in which agents know the policy regime, the fall of inflation and interest rates in the disinflation episode in the early 1980s is better captured by the delayed response of the “learning” specification. Third, the author examines the magnitude of the expectations-formation effect of monetary policy interventions in the “learning” specification by comparing impulse responses to a version of the model in which agents ignore the information contained in current and past monetary policy shocks about the likelihood of a regime shift.
JEL classification: C11, C32, E52
Keywords: Bayesian econometrics, learning, monetary DSGE models
The author would like to thank Thomas Lubik for helpful comments and suggestions. Financial support from the Research Foundation of the University of Pennsylvania is gratefully acknowledged. Gauss programs to implement the empirical analysis conducted in the paper will be available at http://www.econ.upenn.edu/~schorf. This paper was presented at the Monetary Policy and Learning Conference sponsored by the Federal Reserve Bank of Atlanta in March 2003. The views expressed here are the author’s and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the author’s responsibility.
Please address questions regarding content to Frank Schorfheide, Department of Economics, University of Pennsylvania, McNeil Building, 3718 Locust Walk, Philadelphia, Pennsylvania 19104-6297, 215-898-8486, 215-573-2057 (fax), email@example.com.
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