Bayes Estimation of Short-run Coefficients in Dynamic Panel Data Models

This study is concerned with estimating the mean of the coefficients in a dynamic panel data model when the coefficients are assumed to be randomly distributed across cross- sectional units. The authors suggest a Bayes approach to the estimation of such models using Markov chain Monte Carlo methods. They establish the asymptotic equivalence of the Bayes estimator and the mean group estimator proposed by Pesaran and Smith (1995), and show that the Bayes estimator is asymptotically normal for large N (the number of units) and large T (the number of time periods) so long as /N/T60 as both N> and T 64. The performance of the Bayes estimator for the short-run coefficients in dynamic panels is also compared against alternative estimators using both simulated and real data. The Monte Carlo results show that the Bayes estimator has better sampling properties than other estimators for both small and moderate T samples. The analysis of Tobin's q model yields new results.