Dave, Chetan and Feigenbaum, James (2007): Precautionary Learning and Inflationary Biases.

PDF
MPRA_paper_14876.pdf Download (276kB)  Preview 
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 vonNeumann 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 
Language:  English 
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, Macroeconomic Policy, and General Outlook 
Item ID:  14876 
Depositing User:  Chetan Dave 
Date Deposited:  28. Apr 2009 05:16 
Last Modified:  18. Feb 2013 09:53 
References:  [1] 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. [2] Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis (2nd. ed.), Springer. [3] Cho, IK., Sargent, T. J. and Williams, N. (2002), “Escaping Nash Inflation”, Review of Economic Studies, v. 69. [4] 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 [5] Evans, G. and Honkapohja, S. (1999), “Learning Dynamics”, Handbook of Macroeconomics (v. 1A), Elsevier. [6] Evans, G. and Honkapohja, S. (2001), Learning and Expectations in Macroeconomics, Princeton University Press. [7] Kydland, F. and Prescott, E. C. (1977), “Rules Rather Than Discretion: The Inconsistency of Optimal Plans”, Journal of Political Economy, v. 85. [8] Leland, H. E. (1968), “Saving and Uncertainty: The Precautionary Demand for Saving”, The Quarterly Journal of Economics, v. 82. [9] RugeMurcia, F. J. (2003), “Inflation Targeting under Asymmetric Preferences”, Journal of Money, Credit and Banking, v. 35. [10] Sargent, T. J. (1999), The Conquest of American Inflation, Princeton University Press. [11] Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics, Wiley. [12] Zellner, A. (1976), “Bayesian Estimation and Prediction Using Asymmetric Loss Functions”, Journal of the American Statistical Association, v. 81. 22 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/14876 