Individual effects and dynamics in count data models |
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| Affiliation: | 1. Department of Economics, University College London, Gower Street, London WC1E 6BT, UK;2. Institute for Fiscal Studies, 7 Ridgmount Street, London WC1E 7AE, UK;1. Department of Economics, University of North Carolina, Chapel Hill, NC, United States;2. The University of Sydney Business School, Australia;3. St.Petersburg State University, Russian Federation;1. Department of Economics, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA;2. Department of Mathematics and Computer Science, Marburg University, Hans-Meerweinstr., 35032 Marburg, Germany;3. Institute for Mathematics, University of Rostock, 18051 Rostock, Germany;1. Institute of Applied Mathematics, Chinese Academy of Sciences, Haidian District, Zhongguancun, Beijing, China;2. Department of Statistics and Actuarial Science, University of Hong Kong, Pokfulam Road, Hong Kong |
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| Abstract: | In this paper we examine the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables. Use of a linear feedback model is proposed. A quasi-differenced GMM estimator is consistent for the parameters in the dynamic model, but when series are highly persistent, there is a problem of weak instrument bias. An estimator is proposed that utilises pre-sample information of the dependent count variable, which is shown in Monte Carlo simulations to possess desirable small sample properties. The models and estimators are applied to data on US patents and R&D expenditure. |
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