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

Socpus ID

84876893920 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84876893920

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