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

  • Fabio Milani

    ()

    (Department of Economics, University of California-Irvine)

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 a crucial determinant of inflation inertia.

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File URL: http://www.economics.uci.edu/files/docs/workingpapers/2006-07/Milani-03.pdf
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Paper provided by University of California-Irvine, Department of Economics in its series Working Papers with number 060703.

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Length: 19 pages
Date of creation: Dec 2005
Handle: RePEc:irv:wpaper:060703
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  1. Athanasios Orphanides & John C. Williams, 2003. "Imperfect Knowledge, Inflation Expectations, and Monetary Policy," NBER Working Papers 9884, National Bureau of Economic Research, Inc.
  2. Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers 06-5, Federal Reserve Bank of Philadelphia.
  3. James B. Bullard & Kaushik Mitra, 2002. "Learning about monetary policy rules," Working Papers 2000-001, Federal Reserve Bank of St. Louis.
  4. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), pages -, September.
  5. 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, pages -.
  6. 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.
  7. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics 0506013, EconWPA.
  8. Smets, Frank & Wouters, Rafael, 2004. "Comparing Shocks and Frictions in US and Euro Area Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 4750, C.E.P.R. Discussion Papers.
  9. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 507-535, April.
  10. Athanasios Orphanides & John C. Williams, 2005. "Inflation scares and forecast-based monetary policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 498-527, April.
  11. Jean Boivin & Marc P. Giannoni, 2003. "Has Monetary Policy Become More Effective?," NBER Working Papers 9459, National Bureau of Economic Research, Inc.
  12. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: an application to the euro area," Working Paper Research 60, National Bank of Belgium.
  13. 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.
  14. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  15. Marc P. Giannoni & Michael Woodford, 2003. "Optimal Inflation Targeting Rules," NBER Working Papers 9939, National Bureau of Economic Research, Inc.
  16. 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.
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