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Endogeneity and panel data in growth regressions: A Bayesian model averaging approach

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  • León-González, Roberto
  • Montolio, Daniel

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.

Suggested Citation

  • León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    • Handle: RePEc:eee:jmacro:v:46:y:2015:i:c:p:23-39
    DOI: 10.1016/j.jmacro.2015.07.003
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    More about this item

    Keywords

    Bayesian model averaging; Instrumental variables; Panel data; Empirics of growth; Effectiveness of aid;
    All these keywords.

    JEL classification:

    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O2 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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