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.
Authors
![Richard Blundell](/sites/default/files/styles/square_desktop/public/2024-03/Richard%20Blundell%20Head.jpg?itok=ow7e9OkA)
CPP Co-Director
Richard is Co-Director of the Centre for the Microeconomic Analysis of Public Policy (CPP) and Senior Research Fellow at IFS.
![Rachel Cassidy](/sites/default/files/styles/square_desktop/public/2022-08/Rachel_Griffith.jpg?itok=YovGgLq9)
CPP Co-Director, IFS Research Director
Rachel is Research Director and Professor at the University of Manchester. She was made a Dame for services to economic policy and education in 2021.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Research Fellow University of Bristol
Journal article details
- DOI
- 10.1016/S0304-4076(01)00108-7
- Publisher
- Elsevier
- JEL
- C23, C25, O30
- Issue
- Volume 108, Issue 1, May 2002, pages 113-131
Suggested citation
R, Blundell and R, Griffith and F, Windmeijer. (2002). 'Individual effects and dynamics in count data models' 108, Issue 1(2002), pp.113–131.
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