Title
Count Data Stochastic Frontier Models, With An Application To The Patents-R&Amp;D Relationship
Keywords
Discrete data; Halton sequence; Local maximum likelihood; Maximum simulated likelihood; Stochastic frontier analysis
Abstract
This article introduces a new count data stochastic frontier model that researchers can use in order to study efficiency in production when the output variable is a count (so that its conditional distribution is discrete). We discuss parametric and nonparametric estimation of the model, and a Monte Carlo study is presented in order to evaluate the merits and applicability of the new model in small samples. Finally, we use the methods discussed in this article to estimate a production function for the number of patents awarded to a firm given expenditure on R&D. © 2012 Springer Science+Business Media, LLC.
Publication Date
6-1-2013
Publication Title
Journal of Productivity Analysis
Volume
39
Issue
3
Number of Pages
271-284
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/s11123-012-0286-y
Copyright Status
Unknown
Socpus ID
84876893920 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84876893920
STARS Citation
Fé, Eduardo and Hofler, Richard, "Count Data Stochastic Frontier Models, With An Application To The Patents-R&Amp;D Relationship" (2013). Scopus Export 2010-2014. 7039.
https://stars.library.ucf.edu/scopus2010/7039