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Score-driven dynamic patent count panel data models

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URI: https://hdl.handle.net/10016/25168
ISSN: 0165-1765
DOI: https://doi.org/10.1016/j.econlet.2016.10.026
UXXI: AR/0000018590
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score_escribano_EL_2016_ps.pdf (832.96 KB)
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2016-12-01
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Elsevier
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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.
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Research and development, Patent count panel data, Dynamic conditional score, Quasi-maximum likelihood, Maximum-likelihood methods, Poisson counts, Spillovers
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Blazsek, S., Escribano, A. (2016). Score-driven dynamic patent count panel data models. Economics Letters, v. 149, pp. 116-119.
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