Flexible Parametric Models for Long-Tailed Patent Count Distributions

Abstract

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