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

Eduardo Fé and Richard Hofler ()

Journal of Productivity Analysis, 2013, vol. 39, issue 3, 284 pages

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. Copyright Springer Science+Business Media, LLC 2013

Keywords: Discrete data; Stochastic frontier analysis; Local maximum likelihood; Maximum simulated likelihood; Halton sequence; C01; C13; C14; C16; C25; C51 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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Working Paper: Count Data Stochastic Frontier Models, with an application to the patents-R&D Relationship (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:39:y:2013:i:3:p:271-284

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DOI: 10.1007/s11123-012-0286-y

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