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

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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, and General Outlook 
Item ID:  14876 
Depositing User:  Chetan Dave 
Date Deposited:  28 Apr 2009 05:16 
Last Modified:  29 Sep 2019 04:59 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/14876 