A Bias-Corrected Estimation for Dynamic Panel Models in Small Samples

This paper is concerned with the estimation of the autoregressive parameter of dynamic panel data models. We propose a bias-corrected GMM estimator whose bias is smaller than that of many existing GMM estimators. And we propose a small sample corrected estimator of the variance in order to reduce the size distortion of the Wald test. These estimators are easy to calculate and do not require preliminary estimates. The Monte Carlo experiments indicate that in terms of both bias and size distortion, the bias corrected estimator out performs Blundell and Bond's (1998) system estimator even when using Windmeijer's (2005) correction of the estimated variance of the system estimator.