Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach

Roberto Leon-Gonzalez and Daniel Montolio

No 15-16, GRIPS Discussion Papers from National Graduate Institute for Policy Studies

Abstract: Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze panel data models with fixed effects that differ in the set of instruments, exogeneity restrictions, or the set of explanatory variables in the regression. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to investigate the effect of foreign aid on per capita GDP growth. We show that BMA is an effective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.

Pages: 46 pages
Date: 2015-10
New Economics Papers: this item is included in nep-fdg
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Citations: View citations in EconPapers (18)

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Related works:
Journal Article: Endogeneity and panel data in growth regressions: A Bayesian model averaging approach (2015)
Working Paper: Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach (2012)
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