DYNAMIC COUNT DATA MODELS OF TECHNOLOGICAL INNOVATION

This paper examines the application of count data models to firm level panel data on technological innovations. The model we propose exhibit s dynamic feedback and unobserved heterogeneity. We develop a fixed ef fects estimator that generalises the standard Poisson and negative bin omial models allowing for dynamic feedback through both the firm's sto ck of knowledge and its product market power. By using the long pre-sa mple history of innovation information this ''entry stock'' estimator is shown to control for correlated fixed effects and is compared with an alternative nonlinear GMM estimator. We find evidence of history de pendence in innovation activity although variables reflecting the comp any's economic environment are also found to play a major role.