Flexible parametric models for long-tailed patent count distributions

被引：34

作者：

Guo, JQ ^{[1
]}

Trivedi, PK ^{[1
]}

机构：

[1] Indiana Univ, Dept Econ, Bloomington, IN 47405 USA

来源：

OXFORD BULLETIN OF ECONOMICS AND STATISTICS | 2002年 / 64卷 / 01期

关键词：

D O I：

10.1111/1468-0084.00004

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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 binornial regression models. We compare the performance of two relatively new "semiparametric" approaches with two flexible parametric approaches in analysing two patent data sets.

引用

页码：63 / 82

页数：20

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