Bayesian model averaging for dynamic panels with an application to a trade gravity model
将贝叶斯模型平均方法扩展到动态面板数据模型，解决内生性问题，并通过蒙特卡洛模拟验证其有效性，最后应用于贸易引力模型，发现考虑动态性和内生性后，双边贸易的稳健决定因素有所不同。
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.