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

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  • Fabio Milani

    ()
    (Department of Economics, University of California-Irvine)

Abstract

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/economics/docs/workingpapers/2006-07/Milani-03.pdf
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Bibliographic Info

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
Date of revision:
Handle: RePEc:irv:wpaper:060703

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Keywords: Infinite-horizon learning; DSGE model; Bayesian estimation; Non-rational expectations; Inflation persistence; Habit formation;

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References

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  1. Orphanides, Athanasios & Williams, John C., 2003. "Imperfect knowledge, inflation expectations, and monetary policy," CFS Working Paper Series, Center for Financial Studies (CFS) 2003/40, Center for Financial Studies (CFS).
  2. Smets, Frank & Wouters, Raf, 2004. "Comparing shocks and frictions in US and euro area business cycles: a Bayesian DSGE approach," Working Paper Series, European Central Bank 0391, European Central Bank.
  3. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
  4. Marc P. Giannoni & Michael Woodford, 2003. "Optimal Inflation Targeting Rules," NBER Working Papers 9939, National Bureau of Economic Research, Inc.
  5. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 113(1), pages 1-45, February.
  6. James Bullard & Kaushik Mitra, 2002. "Learning about monetary policy rules," Working Papers, Federal Reserve Bank of St. Louis 2000-001, Federal Reserve Bank of St. Louis.
  7. 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, Centre for Dynamic Macroeconomic Analysis 201102, Centre for Dynamic Macroeconomic Analysis.
  8. An, Sungbae & Schorfheide, Frank, 2005. "Bayesian Analysis of DSGE Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5207, C.E.P.R. Discussion Papers.
  9. Orphanides, Athanasios & Williams, John C, 2005. "Inflation Scares and Forecast-Based Monetary Policy," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4844, C.E.P.R. Discussion Papers.
  10. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: an application to the euro area," Working Paper Research, National Bank of Belgium 60, National Bank of Belgium.
  11. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Macroeconomics, EconWPA 0506013, EconWPA.
  12. 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, Federal Reserve Bank of San Francisco, issue Mar.
  13. Jean Boivin & Marc P. Giannoni, 2003. "Has Monetary Policy Become More Effective?," NBER Working Papers 9459, National Bureau of Economic Research, Inc.
  14. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, Elsevier, vol. 53(3), pages 507-535, April.
  15. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 32(10), pages 3148-3165, October.
  16. Preston, Bruce, 2008. "Adaptive learning and the use of forecasts in monetary policy," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 32(11), pages 3661-3681, November.
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Citations

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Cited by:
  1. Fabio Milani, 2006. "The Evolution of the Fed's Inflation Target in an Estimated Model under RE and Learning," Working Papers, University of California-Irvine, Department of Economics 060704, University of California-Irvine, Department of Economics.
  2. Bask, Mikael & Selander, Carina, 2007. "Robust Taylor rules in an open economy with heterogeneous expectations and least squares learning," Research Discussion Papers, Bank of Finland 6/2007, Bank of Finland.
  3. Harrison, Richard & Taylor, Tim, 2012. "Misperceptions, heterogeneous expectations and macroeconomic dynamics," Bank of England working papers, Bank of England 449, Bank of England.
  4. Slobodyan, Sergey & Wouters, Raf, 2012. "Learning in an estimated medium-scale DSGE model," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 36(1), pages 26-46.
  5. Timothy Cogley & Argia M. Sbordone, 2006. "Trend inflation and inflation persistence in the New Keynesian Phillips curve," Staff Reports, Federal Reserve Bank of New York 270, Federal Reserve Bank of New York.
  6. Fazzari, Steven M. & Ferri, Piero & Greenberg, Edward, 2010. "Investment and the Taylor rule in a dynamic Keynesian model," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 34(10), pages 2010-2022, October.
  7. Milani, Fabio, 2009. "Expectations, learning, and the changing relationship between oil prices and the macroeconomy," Energy Economics, Elsevier, Elsevier, vol. 31(6), pages 827-837, November.
  8. Chevillon, Guillaume & Massmann, Michael & Mavroeidis, Sophocles, 2010. "Inference in models with adaptive learning," Journal of Monetary Economics, Elsevier, Elsevier, vol. 57(3), pages 341-351, April.
  9. Krisztina Molnar & Sergio Santoro, 2006. "Optimal Monetary Policy when Agents are Learning," Computing in Economics and Finance 2006, Society for Computational Economics 40, Society for Computational Economics.
  10. John M. Roberts, 2007. "Learning, Sticky Inflation, and the Sacrifice Ratio," Kiel Working Papers 1365, Kiel Institute for the World Economy.
  11. Claudio Borio & Piti Disyatat & Mikael Juselius, 2014. "A parsimonious approach to incorporating economic information in measures of potential output," BIS Working Papers 442, Bank for International Settlements.
  12. Milani, Fabio, 2011. "The impact of foreign stock markets on macroeconomic dynamics in open economies: A structural estimation," Journal of International Money and Finance, Elsevier, Elsevier, vol. 30(1), pages 111-129, February.
  13. Fabio Milani, 2009. "The Effect of Global Output on U.S. Inflation and Inflation Expectations: A Structural Estimation," Working Papers, University of California-Irvine, Department of Economics 080920, University of California-Irvine, Department of Economics.

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