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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. Andrew Levin & Volker Wieland & John C. Williams, 2003. "The Performance of Forecast-Based Monetary Policy Rules Under Model Uncertainty," American Economic Review, American Economic Association, vol. 93(3), pages 622-645, June.
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  5. Timothy Cogley & Thomas J. Sargent, 2008. "Anticipated Utility And Rational Expectations As Approximations Of Bayesian Decision Making," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(1), pages 185-221, 02.
  6. Volker Wieland, 1998. "Monetary policy and uncertainty about the natural unemployment rate," Finance and Economics Discussion Series 1998-22, Board of Governors of the Federal Reserve System (U.S.).
  7. Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-60, September.
  8. 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.).
  9. 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.
  10. Massimo Guidolin & Allan Timmerman, 2005. "Properties of equilibrium asset prices under alternative learning schemes," Working Papers 2005-009, Federal Reserve Bank of St. Louis.
  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.
  12. McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(01), pages 119-139, February.
  13. 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.
  14. 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.
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