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Learning and monetary policy shifts

  • Frank Schorfheide

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.

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Paper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 2003-23.

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Date of creation: 2003
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Handle: RePEc:fip:fedawp:2003-23
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  1. Marco Del Negro & Frank Schorfheide, 2002. "Priors from general equilibrium models for VARs," Working Paper 2002-14, Federal Reserve Bank of Atlanta.
  2. Clarida, R. & Gali, J. & Gertler, M., 1998. "Monetary Policy Rules and Macroeconomic Stability: Evidence and some Theory," Working Papers 98-01, C.V. Starr Center for Applied Economics, New York University.
  3. David Andolfatto & Paul Gomme, 1997. "Monetary policy regimes and beliefs," Discussion Paper / Institute for Empirical Macroeconomics 118, Federal Reserve Bank of Minneapolis.
  4. Robert G. King, 2000. "The new IS-LM model : language, logic, and limits," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 45-103.
  5. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
  6. Sharon Kozicki & P.A. Tinsley, 1997. "Shifting endpoints in the term structure of interest rates," Research Working Paper 97-08, Federal Reserve Bank of Kansas City.
  7. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
  8. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  9. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  10. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2004. "Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach," PIER Working Paper Archive 04-001, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  11. Thomas Lubik & Frank Schorfheide, 2002. "Testing for Indeterminacy in Linear Rational Expectations Models," Computing in Economics and Finance 2002 214, Society for Computational Economics.
  12. David Andolfatto & Scott Hendry & Kevin Moran, 2004. "Inflation Expectations and Learning about Monetary Policy," DNB Staff Reports (discontinued) 121, Netherlands Central Bank.
  13. Erceg, Christopher J. & Levin, Andrew T., 2003. "Imperfect credibility and inflation persistence," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 915-944, May.
  14. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  15. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
  16. Cogley, Timothy & Sargent, Thomas J., 2005. "The conquest of U.S. inflation: learning and robustness to model uncertainty," Working Paper Series 0478, European Central Bank.
  17. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, August.
  18. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The Conquest of South American Inflation," NBER Working Papers 12606, National Bureau of Economic Research, Inc.
  19. Cooley, Thomas F & LeRoy, Stephen F & Raymon, Neil, 1984. "Econometric Policy Evaluation: Note," American Economic Review, American Economic Association, vol. 74(3), pages 467-70, June.
  20. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
  21. John Geweke, 1998. "Using simulation methods for Bayesian econometric models: inference, development, and communication," Staff Report 249, Federal Reserve Bank of Minneapolis.
  22. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  23. Giorgio Primiceri, 2005. "Why Inflation Rose and Fell: Policymakers' Beliefs and US Postwar Stabilization Policy," NBER Working Papers 11147, National Bureau of Economic Research, Inc.
  24. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
  25. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  26. Christopher Sims & Tao Zha, 2002. "Macroeconomic switching," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
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