Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model

Download or read book Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model written by Huigang Chen and published by International Monetary Fund. This book was released on 2011-10-01 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.