Flexible Parametric Models for Long-Tailed Patent Count Distributions
26 Pages Posted: 19 Mar 2002
Date Written: November 2000
This article explores alternative approaches to modeling the relationship between the number of patents and research and development expenditure. Patent counts typically exhibit long upper tails that are inadequately mod-eled by standard Poisson and negative binomial regression models. We compare the performance of two relatively new "semiparametric" approaches with two exible parametric approaches in analyzing two patent data sets.
Keywords: Series expansions, Semiparametric models, Finite mix-tures, Overdispersion, Patents-R&D, Poisson-inverse Gaussian
JEL Classification: c25
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