Flexible Parametric Models for Long-Tailed Patent Count Distributions

26 Pages Posted: 19 Mar 2002

See all articles by Pravin K. Trivedi

Pravin K. Trivedi

Indiana University Purdue University Indianapolis (IUPUI) - Department of Economics

Jie Qun Guo

Interactive Data Pricing and Reference Data, Inc.

Multiple version iconThere are 2 versions of this paper

Date Written: November 2000

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

Suggested Citation

Trivedi, Pravin K. and Guo, Jie Qun, Flexible Parametric Models for Long-Tailed Patent Count Distributions (November 2000). Available at SSRN: https://ssrn.com/abstract=303921 or http://dx.doi.org/10.2139/ssrn.303921

Pravin K. Trivedi (Contact Author)

Indiana University Purdue University Indianapolis (IUPUI) - Department of Economics ( email )

Wylie Hall
Bloomington, IN 47405-2100
United States

Jie Qun Guo

Interactive Data Pricing and Reference Data, Inc. ( email )

New York, NY 10007
United States

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