Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach
Iain Cockburn () and
Martin L Puterman
Journal of Business & Economic Statistics, 1998, vol. 16, issue 1, 27-41
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 (1) 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 (2) when the mixing distribution is incorrectly specified, parameter estimates remain unbiased but have inflated variances.
References: Add references at CitEc
Citations: View citations in EconPapers (44) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:16:y:1998:i:1:p:27-41
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano
More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().