On the Bias of the LSDV Estimator in Dynamic Panel Data Models with Endogenous Regressors

Abstract

This paper studies the behaviour of the bias corrected LSDV estimator and GMM-based estimators in dynamic panel data models with endogenous regressors. We obtain an expansion of the conditional bias of the LSDV estimator with the leading term coinciding with the one in the expansion from (Kiviet, 1995) and (Kiviet, 1999). Nevertheless, our simulations suggest that in the presence of endogenous regressors the performance of the corrected LSDV estimator can be low. This indicates that although the bias has similar structure whether or not the exogeneity assumption holds, the approximation technique that the LSDVc estimator is based on can work poorly in the endogenous case. GMM-based estimators also have low performance in our experiment.