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2015-12

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cc-by-nc-nd (c) Elsevier, 2015
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/122433

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

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https://doi.org/10.1016/j.jmacro.2015.07.003

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.

Subject

Estadística bayesiana, Anàlisi de dades de panel, Anàlisi de regressió, Mètode de Montecarlo, Processos de Markov

Subject (English)

Bayesian statistical decision, Panel analysis, Regression analysis, Monte Carlo method, Markov processes

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Articles publicats en revistes (Economia)

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LEÓN-GONZÁLEZ, Roberto and MONTOLIO, Daniel. Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach. Journal of Macroeconomics. 2015. Vol. 46, num. December, pags. 23-39. ISSN 0164-0704. [consulted: 11 of August of 2026]. Available at: https://hdl.handle.net/2445/122433

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