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Bayesian model averaging for dynamic panels with an application to a trade gravity model
Authors:Huigang Chen  Alin Mirestean
Institution:1. MarketShare Partners;2. International Monetary Fund
Abstract:We extend the Bayesian Model Averaging (BMA) framework to dynamic panel data models with endogenous regressors using a Limited Information Bayesian Model Averaging (LIBMA) methodology. Monte Carlo simulations confirm the asymptotic performance of our methodology both in BMA and selection, with high posterior inclusion probabilities for all relevant regressors, and parameter estimates very close to their true values. In addition, we illustrate the use of LIBMA by estimating a dynamic gravity model for bilateral trade. Once model uncertainty, dynamics, and endogeneity are accounted for, we find several factors that are robustly correlated with bilateral trade. We also find that applying methodologies that do not account for either dynamics or endogeneity (or both) results in different sets of robust determinants.
Keywords:Bayesian model averaging  dynamic panels  generalized method of moments  gravity model  model uncertainty  robustness
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