A Bayesian Approach to Model Uncertainty
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.
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"Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach,"
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- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
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- Carlos Leite & Charalambos G. Tsangarides & Dhaneshwar Ghura, 2002. "Is Growth Enough? Macroeconomic Policy and Poverty Reduction," IMF Working Papers 02/118, International Monetary Fund.
- Kim, Jae-Young, 2002. "Limited information likelihood and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 175-193, March.
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