Flexible Parametric Models for Long-Tailed Patent Count Distributions

20 Pages Posted: 5 Dec 2002

See all articles by Jie Qun Guo

Jie Qun Guo

Interactive Data Pricing and Reference Data, Inc.

Pravin K. Trivedi

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

Multiple version iconThere are 2 versions of this paper

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 modeled by standard Poisson and negative binomial regression models. We compare the performance of two relatively new "semiparametric" approaches with two flexible parametric approaches in analysing two patent data sets.

Suggested Citation

Guo, Jie Qun and Trivedi, Pravin K., Flexible Parametric Models for Long-Tailed Patent Count Distributions. Oxford Bulletin of Economics and Statistics, Vol. 64, pp. 63-82, 2002. Available at SSRN: https://ssrn.com/abstract=312816

Jie Qun Guo (Contact Author)

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

New York, NY 10007
United States

Pravin K. Trivedi

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

Wylie Hall
Bloomington, IN 47405-2100
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
18
Abstract Views
1,012
PlumX Metrics