A Bayesian Approach to Model Uncertainty
AbstractThis 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.
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Bibliographic InfoPaper provided by International Monetary Fund in its series IMF Working Papers with number 04/68.
Date of creation: 01 Apr 2004
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-22 (All new papers)
- NEP-ECM-2005-10-22 (Econometrics)
- NEP-ETS-2005-10-22 (Econometric Time Series)
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