A Bayesian Approach to Model Uncertainty

Author/Editor:

Charalambos G Tsangarides

Publication Date:

April 1, 2004

Electronic Access:

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Summary:

This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it simultaneously addresses the biases associated with endogenous and omitted variables by incorporating a panel data systems Generalized Method of Moments estimator. Practical applications of the developed methodology are discussed, including testing for the robustness of explanatory variables in the analyses of the determinants of economic growth and poverty.

Series:

Working Paper No. 04/68

Subject:

English

Publication Date:

April 1, 2004

ISBN/ISSN:

9781451849028/1018-5941

Stock No:

WPIEA0682004

Format:

Paper

Pages:

21

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