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The stability of macroeconomic systems with Bayesian learners

  • James B. Bullard
  • Jacek Suda

We study abstract macroeconomic systems in which expectations play an important role. Consistent with the recent literature on recursive learning and expectations, we replace the agents in the economy with econometricians. Unlike the recursive learning literature, however, the econometricians in the analysis here are Bayesian learners. We are interested in the extent to which expectational stability remains the key concept in the Bayesian environment. We isolate conditions under which versions of expectational stability conditions govern the stability of these systems just as in the standard case of recursive learning. We conclude that the more sophisticated Bayesian learning schemes do not alter the essential expectational stability findings in the literature.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2008-043.

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Date of creation: 2008
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Handle: RePEc:fip:fedlwp:2008-043
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  1. Williams, John C. & Levin, Andrew T. & Wieland, Volker, 2001. "The performance of forecast-based monetary policy rules under model uncertainty," Working Paper Series 0068, European Central Bank.
  2. McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(01), pages 119-139, February.
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  8. Wieland, Volker, 1999. "Monetary policy, parameter uncertainty and optimal learning," ZEI Working Papers B 09-1999, ZEI - Center for European Integration Studies, University of Bonn.
  9. George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," University of Oregon Economics Department Working Papers 2005-17, University of Oregon Economics Department, revised 18 May 2008.
  10. Thomas J. Sargent & Noah William, 2005. "Impacts of Priors on Convergence and Escapes from Nash Inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
  11. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-86, August.
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  13. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  14. Athanasios Orphanides & Volker Wieland, 1999. "Efficient monetary policy design near price stability," Finance and Economics Discussion Series 1999-67, Board of Governors of the Federal Reserve System (U.S.).
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