The Effects of Dynamic Feedbacks on Ls and Mm Estimator Accuracy in Panel Data Models

Abstract

The finite sample behaviour is analysed of particular least squares (LS) and method of moments (MM) estimators in panel data models with individual effects and both a lagged dependent variable regressor and another explanatory variable which may be affected by lagged feedbacks from the dependent variable. Asymptotic expansions indicate that the order of magnitude of bias of (generalized) MM estimators tends to increase with the number of moment conditions exploited. For various estimation procedures we examine the analytical effects of feedbacks and other model characteristics such as prominence of individual effects. Simulation results corroborate our theoretical findings and show that in small samples of models with dynamic feedbacks none of the techniques examined dominates. However, a simple bias corrected LS estimator which presupposes strict exogeneity is found to be rather robust, showing often smaller root mean squared errors than GMM.

Keywords: asymptotic expansions, bias approximation, dynamic panel data model, feedback mechanisms, Monte Carlo simulation

JEL Classification: C13, C23