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Learning and Time-Varying Macroeconomic Volatility

  • Fabio Milani

    ()

    (Department of Economics, University of California-Irvine)

This paper presents a DSGE model in which agents' learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors. The model is estimated using likelihood-based Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system. The estimation results show that private agents appear to have often switched to constant-gain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the Great Moderation. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find time-varying volatility in the exogenous shocks, even when these have constant variance by construction.

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File URL: http://www.economics.uci.edu/files/docs/workingpapers/2007-08/Milani-02.pdf
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Paper provided by University of California-Irvine, Department of Economics in its series Working Papers with number 070802.

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Length: 42 pages
Date of creation: May 2007
Date of revision:
Handle: RePEc:irv:wpaper:070802
Contact details of provider: Postal: Irvine, CA 92697-3125
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Web page: http://www.economics.uci.edu/

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  1. Athanasios Orphanides & John Williams, 2004. "Imperfect Knowledge, Inflation Expectations, and Monetary Policy," NBER Chapters, in: The Inflation-Targeting Debate, pages 201-246 National Bureau of Economic Research, Inc.
  2. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  3. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
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  19. Fabio Milani, 2006. "A Bayesian DSGE Model with Infinite-Horizon Learning: Do "Mechanical" Sources of Persistence Become Superfluous?," International Journal of Central Banking, International Journal of Central Banking, vol. 2(3), September.
  20. Athanasios Orphanides & John C. Williams, 2007. "Inflation Targeting under Imperfect Knowledge," Central Banking, Analysis, and Economic Policies Book Series, in: Frederic S. Miskin & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.), Monetary Policy under Inflation Targeting, edition 1, volume 11, chapter 4, pages 077-123 Central Bank of Chile.
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  28. Marcet, A. & Nicolini, J.P., 1997. "Recurrent Hyperinflations and Learning," Papers 9721, Centro de Estudios Monetarios Y Financieros-.
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  36. Domenico Colucci & Vincenzo Valori, 2004. "Adaptive learning in the Cobweb with an endogenous gain sequence," Working Papers - Mathematical Economics 2004-01, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
  37. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
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