Alvaro Escribano
Universidad Carlos III de Madrid, Spain
Title: Score-driven dynamic patent count panel data models
Biography
Biography: Alvaro Escribano
Abstract
In this paper, we propose the use of Dynamic Conditional Score (DCS) count panel data models. We compare the statistical performance of the static model with different dynamic models: finite distributed lag, exponential feedback and different DCS models. For DCS, we consider random walk or quasiautoregressive
dynamics. We use panel data for a large cross section of United States firms for period 1979–2000, and the Poisson quasi-maximum likelihood estimator with fixed effects. The empirical results suggest that DCS has the best statistical performance.