Score-driven dynamic patent count panel data models Articles uri icon

publication date

  • December 2016

start page

  • 116

end page

  • 119

volume

  • 149

international standard serial number (ISSN)

  • 0165-1765

electronic international standard serial number (EISSN)

  • 1873-7374

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 quasi-autoregressive 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. (C) 2016 Elsevier B.V. All rights reserved.

keywords

  • Research and development
    Patent count panel data
    Dynamic conditional score
    Quasi-maximum likelihood
    Maximum-likelihood methods
    Poisson counts
    Spillovers