Dave, Chetan and Feigenbaum, James (2007): Precautionary Learning and Inflationary Biases.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Precautionary Learning and Inflationary Biases|
|Keywords:||Least squares learning, time inconsistency, statistical decision making|
|Subjects:||C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory
E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
|Depositing User:||Chetan Dave|
|Date Deposited:||28. Apr 2009 05:16|
|Last Modified:||18. Feb 2013 09:53|
 Barro, R. J. and Gordon, D. R. (1983), “A Positive Theory of Monetary Policy in a Natural Rate Model”, Journal of Political Economy, v. 91.
 Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis (2nd. ed.), Springer.
 Cho, I-K., Sargent, T. J. and Williams, N. (2002), “Escaping Nash Inflation”, Review of Economic Studies, v. 69.
 Cukierman, A. (2002), “Are Contemporary Central Banks Transparent about Economic Models and Objectives and What Difference Does it Make?”, Federal Reserve Bank of St. Louis Review, v. 84. 21
 Evans, G. and Honkapohja, S. (1999), “Learning Dynamics”, Handbook of Macroeconomics (v. 1A), Elsevier.
 Evans, G. and Honkapohja, S. (2001), Learning and Expectations in Macroeconomics, Princeton University Press.
 Kydland, F. and Prescott, E. C. (1977), “Rules Rather Than Discretion: The Inconsistency of Optimal Plans”, Journal of Political Economy, v. 85.
 Leland, H. E. (1968), “Saving and Uncertainty: The Precautionary Demand for Saving”, The Quarterly Journal of Economics, v. 82.
 Ruge-Murcia, F. J. (2003), “Inflation Targeting under Asymmetric Preferences”, Journal of Money, Credit and Banking, v. 35.
 Sargent, T. J. (1999), The Conquest of American Inflation, Princeton University Press.
 Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics, Wiley.  Zellner, A. (1976), “Bayesian Estimation and Prediction Using Asymmetric Loss Functions”, Journal of the American Statistical Association, v. 81. 22