Peiming Wang () (Nanyang Business School, Nanyang Technological University, Singapore) Iain Cockburn Martin L. Puterman () (Faculty of Commerce and Business Administration, University of British Columbia)
Additional information is available for the following
registered author(s):
We analyze cross-sectional patent data using a finite mixed Poisson regression model with covariates in Poisson rates and mixing probabilities. Maximum likelihood estimation based on the EM and quasi-Newton algorithms, a model selection procedure, residual analysis and goodness-of-fit tests are discussed. This model is applied to data on the relationship between technological innovation and R&D research. Results are compared in several ways to those obtained using alternative models for overdispersion. Monte Carlo studies show among other things that the selection criteria usually choose the correct model and that when the mixing distribution is incorrectly specified, estimates of parameters remain unbiased but are inefficient.
Download Info
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page
whether it is in fact available.
3. Perform a search for a similarly titled item that would be
available.