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

Boris Lokshin


This paper extends the LSDV bias-corrected estimator in [Bun, M., Carree, M.A. 2005. Bias-corrected estimation in dynamic panel data models, Journal of Business and Economic Statistics, 23(2): 200-10] to unbalanced panels and discusses the analytic method of obtaining the solution. Using a Monte Carlo approach the paper 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 labor productivity in an unbalanced panel of innovating firms.

Key words: Bias correction, unbalanced panel data, GMM; dynamic model

JEL codes: C23

UNU-MERIT Working Papers ISSN 1871-9872