We examine the impact of internal and external R&D on labor productivity in a 6-year panel of 304 innovating firms. We apply a dynamic linear panel data model that allows for decreasing returns to scale in internal and external R&D with a non-linear approximation of changes in the knowledge...

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. Our residual-based GMM t-test (hereafter: t-test) differs from the m2 and Sargan's over-identifying restriction (hereafter: Sargan test) in Arellano and Bond (1991), both...

This paper addresses the many instruments problem, i.e. (1) the trade-off between the bias and the efficiency of the GMM estimator, and (2) inaccuracy of inference, in dynamic panel data models where unobservable heterogeneity may be large. We find that if we use all the instruments in levels,...

The system GMM estimator in dynamic panel data models which combines two moment conditions, i.e., for the differenced equation and for the model in levels, is known to be more efficient than the first-difference GMM estimator. However, an initial optimal weight matrix is not known for the system...