Count data stochastic frontier models, with an application to the patents-R&D relationship

Eduardo Fé, Richard Hofler

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

LanguageEnglish
Pages271-284
Number of pages14
JournalJournal of Productivity Analysis
Volume39
Issue number3
DOIs
Publication statusPublished - 20 Jun 2013

Fingerprint

patent
production function
expenditures
firm
efficiency
Patents
Count data
Stochastic frontier model
Nonparametric estimation
Monte Carlo study
Production function
Expenditure
Small sample
Conditional distribution

Keywords

  • discrete data
  • halton sequence
  • local maximum likelihood
  • maximum simulated likelihood
  • stochastic frontier analysis

Cite this

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Count data stochastic frontier models, with an application to the patents-R&D relationship. / Fé, Eduardo; Hofler, Richard.

In: Journal of Productivity Analysis, Vol. 39, No. 3, 20.06.2013, p. 271-284.

Research output: Contribution to journalArticle

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