Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods

Download or read book Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods written by Alin Mirestean and published by International Monetary Fund. This book was released on 2009-04 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA 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 selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.