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

Eduardo Fé, Richard Hofler

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)271-284
Number of pages14
JournalJournal of Productivity Analysis
Issue number3
Publication statusPublished - 20 Jun 2013


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

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