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Precautionary Learning and Inflationary Biases

Author

Listed:
  • Dave, Chetan
  • Feigenbaum, James

Abstract

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a statistical decision making context, this loss function can be understood as a second order approximation to a von-Neumann Morgenstern utility function. This paper considers instead the implications for adaptive learning of a third order approximation. The resulting asymmetry leads the estimator to put more weight on avoiding mistakes in one direction as opposed to the other. As a precaution against making a more costly mistake, a statistician biases his estimates in the less costly direction by an amount proportional to the variance of the estimate. We investigate how this precautionary bias will affect learning dynamics in a model of inflationary biases. In particular we find that it is possible to maintain a lower long run inflation rate than could be obtained in a time consistent rational expectations equilibrium.

Suggested Citation

  • Dave, Chetan & Feigenbaum, James, 2007. "Precautionary Learning and Inflationary Biases," MPRA Paper 14876, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:14876
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    File URL: https://mpra.ub.uni-muenchen.de/14876/1/MPRA_paper_14876.pdf
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    References listed on IDEAS

    as
    1. Alex Cukierman, 2002. "Are contemporary central banks transparent about economic models and objectives and what difference does it make?," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 15-36.
    2. Barro, Robert J & Gordon, David B, 1983. "A Positive Theory of Monetary Policy in a Natural Rate Model," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 589-610, August.
    3. Ruge-Murcia, Francisco J, 2003. " Inflation Targeting under Asymmetric Preferences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(5), pages 763-785, October.
    4. William Poole & Robert H. Rasche, 2002. "Flation," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 1-6.
      • William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
    5. In-Koo Cho & Noah Williams & Thomas J. Sargent, 2002. "Escaping Nash Inflation," Review of Economic Studies, Oxford University Press, vol. 69(1), pages 1-40.
    6. Evans, George W. & Honkapohja, Seppo, 1999. "Learning dynamics," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 7, pages 449-542 Elsevier.
    7. Kydland, Finn E & Prescott, Edward C, 1977. "Rules Rather Than Discretion: The Inconsistency of Optimal Plans," Journal of Political Economy, University of Chicago Press, vol. 85(3), pages 473-491, June.
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    More about this item

    Keywords

    Least squares learning; time inconsistency; statistical decision making;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook

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