Further results on bias in dynamic unbalanced panel data models with an application to firm R&D investment

Abstract

This article extends the LSDV bias-corrected estimator in (Bun and Carree, 2005) to unbalanced panels and discusses the analytic method of obtaining the solution. Using a Monte Carlo approach the article compares the performance of this estimator with three other available techniques for dynamic panel data models. Simulation reveals that LSDV-bc estimator is a good choice except for samples with small T, where it may be unpractical. The methodology is applied to examine the impact of internal and external R&D on labour productivity in an unbalanced panel of innovating firms.