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The stability of macroeconomic systems with Bayesian learners

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  • James B. Bullard
  • Jacek Suda

Abstract

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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Bibliographic Info

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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Keywords: Rational expectations (Economic theory);

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  1. Volker Wieland, 1999. "Monetary policy, parameter uncertainty and optimal learning," Finance and Economics Discussion Series 1999-48, Board of Governors of the Federal Reserve System (U.S.).
  2. McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(01), pages 119-139, February.
  3. George W. Evans & Seppo Honkapohja & Noah Williams, 2010. "Generalized Stochastic Gradient Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(1), pages 237-262, 02.
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  5. Bullard, James, 1992. "Time-varying parameters and nonconvergence to rational expectations under least squares learning," Economics Letters, Elsevier, vol. 40(2), pages 159-166, October.
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  7. Tim W. Cogley & Thomas J. Sargent, 2005. "Anticipated Utility and Rational Expectations as Approximations of Bayesian Decision Making," Working Papers 523, University of California, Davis, Department of Economics.
  8. Wieland, Volker, 2003. "Monetary Policy and Uncertainty about the Natural Unemployment Rate," CEPR Discussion Papers 3811, C.E.P.R. Discussion Papers.
  9. Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-60, September.
  10. 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.
  11. Brian Sack & Volker Wieland, 1999. "Interest-rate smoothing and optimal monetary policy: a review of recent empirical evidence," Finance and Economics Discussion Series 1999-39, Board of Governors of the Federal Reserve System (U.S.).
  12. 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.
  13. 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.
  14. Orphanides, Athanasios & Wieland, Volker, 2000. "Efficient Monetary Policy Design near Price Stability," Journal of the Japanese and International Economies, Elsevier, vol. 14(4), pages 327-365, December.
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Cited by:
  1. Carravetta, Francesco & Sorge, Marco M., 2013. "Model reference adaptive expectations in Markov-switching economies," Economic Modelling, Elsevier, vol. 32(C), pages 551-559.

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