Further results on bias in dynamic unbalanced panel data models with an application to firm R&D investment
Boris Lokshin
#2008-039
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