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Modeling Model Uncertainty

  • Alexei Onatski

    (Columbia University)

  • Noah Williams

    (Princeton University and NBER)

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different "robust" policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real-time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the U.S. economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies. (JEL: E52, C32, D81) Copyright (c) 2003 The European Economic Association.

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Article provided by MIT Press in its journal Journal of the European Economic Association.

Volume (Year): 1 (2003)
Issue (Month): 5 (09)
Pages: 1087-1122

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Handle: RePEc:tpr:jeurec:v:1:y:2003:i:5:p:1087-1122
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  1. Alexei Onatski & James H. Stock, 1999. "Robust monetary policy under model uncertainty in a small model of the U.S. economy," Proceedings, Federal Reserve Bank of San Francisco.
  2. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
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