Analysis of Patent Data—A Mixed-Poisson-Regression-Model Approach

Peiming Wang · lain M. Cockburn · Martin L. Puterman

119 citations0/0 files storedJournal of Business and Economic Statistics

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Intellectual Property and Patents Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models

Abstract

Count-data models are used to analyze the relationship between patents and research and development spending at the firm level, accounting for overdispersion using a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities. Maximum likelihood estimation using the EM and quasi-Newton algorithms is discussed. Monte Carlo studies suggest that (a) penalized likelihood criteria are a reliable basis for model selection and can be used to determine whether continuous or finite support for the mixing distribution is more appropriate and (b) when the mixing distribution is incorrectly specified, parameter estimates remain unbiased but have inflated variances.

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References

1990Patent Statistics as Economic Indicators: A Survey 1984Econometric Models for Count Data with an Application to the Patents-R & D Relationship 1984A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data 1994Measuring Competence? Exploring Firm Effects in Pharmaceutical Research 1989Empirical studies of innovation and market structure 1986Patents and R and D: Is There a Lag?1989Chapter 18 Empirical studies of innovation and market structure 1984Patents and R&D: Is There A Lag?

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