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Title: Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach
Author: León-González, Roberto
Montolio, Daniel
Keywords: Estadística bayesiana
Anàlisi de dades de panel
Anàlisi de regressió
Mètode de Montecarlo
Processos de Markov
Bayesian statistical decision
Panel analysis
Regression analysis
Monte Carlo method
Markov processes
Issue Date: Dec-2015
Publisher: Elsevier
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.
Note: Versió postprint del document publicat a:
It is part of: Journal of Macroeconomics, 2015, vol. 46, num. December, p. 23-39
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ISSN: 0164-0704
Appears in Collections:Articles publicats en revistes (Economia)

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