A Bayesian Approach to Model Uncertainty

April 1, 2004

Preview Citation

Format: Chicago

Charalambos G Tsangarides. A Bayesian Approach to Model Uncertainty, (USA: International Monetary Fund, 2004) accessed November 21, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

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.

Subject: Agroindustries, Bayesian models, Estimation techniques

Keywords: Least squares, WP

Publication Details

Pages:

21
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2004/068
Stock No:

WPIEA0682004
ISBN:

9781451849028
ISSN:

1018-5941

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