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Score-driven dynamic patent count panel data models

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URI: https://hdl.handle.net/10016/23458
ISSN: 2340-5031
UXXI: DT/0000001478
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2016-07
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This paper suggests new Dynamic Conditional Score (DCS) count panel data models. We compare the statistical performance of static model, finite distributed lag model, exponential feedback model and different DCS count panel data models. For DCS we consider random walk and quasi-autoregressive formulations of dynamics. We use panel data for a large cross section of United States firms for period 1979 to 2000. We estimate models by using the Poisson quasi-maximum likelihood estimator with fixed effects. The estimation results and diagnostics tests suggest that the statistical performance of DCS-QAR is superior to that of alternative models.
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research and development, count panel data, dynamic conditional score, quasi-maximum likelihood
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DE - Working Papers. Economics. WE