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A Bayesian DSGE Model with Infinite-Horizon Learning: Do "Mechanical" Sources of Persistence Become Superfluous?

  • Milani, Fabio

This paper estimates a monetary DSGE model with learning introduced from the primitive assumptions. The model nests infinite-horizon learning and features, such as habit formation in consumption and inflation indexation, that are essential for the model fit under rational expectations. I estimate the DSGE model by Bayesian methods, obtaining estimates of the main learning parameter, the constant gain, jointly with the deep parameters of the economy. The results show that relaxing the assumption of rational expectations in favor of learning may render mechanical sources of persistence superfluous. In particular, learning appears to be a crucial determinant of inflation inertia.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 809.

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Date of creation: 28 Jun 2006
Date of revision:
Publication status: Published in International Journal of Central Banking Number 3.Volume(2006): pp. 87-106
Handle: RePEc:pra:mprapa:809
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  1. Marc Giannoni & Michael Woodford, 2004. "Optimal Inflation-Targeting Rules," NBER Chapters, in: The Inflation-Targeting Debate, pages 93-172 National Bureau of Economic Research, Inc.
  2. Kaushik Mitra & James Bullard, . "Learning About Monetary Policy Rules," Discussion Papers 00/41, Department of Economics, University of York.
  3. Orphanides, Athanasios & Williams, John C., 2003. "Imperfect knowledge, inflation expectations, and monetary policy," CFS Working Paper Series 2003/40, Center for Financial Studies (CFS).
  4. Bruce Preston, 2003. "Learning about monetary policy rules when long-horizon expectations matter," FRB Atlanta Working Paper 2003-18, Federal Reserve Bank of Atlanta.
  5. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
  6. Smets, Frank & Wouters, Rafael, 2004. "Forecasting with a Bayesian DSGE Model: An Application to the Euro Area," CEPR Discussion Papers 4749, C.E.P.R. Discussion Papers.
  7. Fabio Milani, 2005. "Learning, Monetary Policy Rules, and Macroeconomic Stability," Macroeconomics 0508019, EconWPA.
  8. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Working Papers 050607, University of California-Irvine, Department of Economics.
  9. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
  10. Seppo Honkapohja & Kaushik Mitra & George W. Evans, 2011. "Notes on Agents¡¯ Behavioral Rules Under Adaptive Learning and Studies of Monetary Policy," CDMA Working Paper Series 201102, Centre for Dynamic Macroeconomic Analysis.
  11. Athanasios Orphanides & John C. Williams, 2003. "Inflation scares and forecast-based monetary policy," FRB Atlanta Working Paper 2003-21, Federal Reserve Bank of Atlanta.
  12. Frank Smets & Raf Wouters, 2004. "Comparing shocks and frictions in US and euro area business cycles: a Bayesian DSGE approach," Working Paper Research 61, National Bank of Belgium.
  13. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper Series WP-01-08, Federal Reserve Bank of Chicago.
  14. Frank Smets & Raf Wouters, 2002. "Monetary policy in an estimated stochastic dynamic general equilibrium model of the Euro area," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  15. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 507-535, April.
  16. Preston, Bruce, 2008. "Adaptive learning and the use of forecasts in monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 32(11), pages 3661-3681, November.
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