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 exhibits dynamic feedback and unobserved heterogeneity. We develop a fixed effects estimator that generalises the standard Poisson and negative binomial models allowing for dynamic feedback through both the firm's stock of knowledge and its product market power. By using the long pre-sample 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 dependence in innovation activity although variables reflecting the company's economic environment are also found to play a major role.