Dynamic Count Data Models of Technological Innovation
提出一种动态计数数据模型，用于分析企业层面技术创新的面板数据，通过固定效应估计器控制未观测异质性，发现创新活动存在历史依赖性，同时经济环境变量也起重要作用。
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.