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Baysian Model Averaging, Learning and Model Selection

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  • Mitra, Kaushik
  • Evans, George W.
  • Honkapohja, Seppo

Abstract

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

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Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2012-11.

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Date of creation: 2012
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Handle: RePEc:edn:sirdps:314

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Keywords: Learning dynamics; Bayesian model averaging; grain of truth; self-referential systems;

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  1. Thomas J. Sargent & Noah William, 2005. "Impacts of Priors on Convergence and Escapes from Nash Inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
  2. Bullard, James, 1992. "Time-varying parameters and nonconvergence to rational expectations under least squares learning," Economics Letters, Elsevier, vol. 40(2), pages 159-166, October.
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