Score-driven dynamic patent count panel data models
Szabolcs Blazsek () and
Alvaro Escribano ()
Economics Letters, 2016, vol. 149, issue C, 116-119
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.
Keywords: Research and development; Patent count panel data; Dynamic conditional score; Quasi-maximum likelihood (search for similar items in EconPapers)
JEL-codes: C33 C35 C51 C52 O3 (search for similar items in EconPapers)
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Working Paper: Score-driven dynamic patent count panel data models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:149:y:2016:i:c:p:116-119
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