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

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  • Eduardo Fé

    ()

  • Richard Hofler

    ()

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

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File URL: http://hdl.handle.net/10.1007/s11123-012-0286-y
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Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 39 (2013)
Issue (Month): 3 (June)
Pages: 271-284

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Handle: RePEc:kap:jproda:v:39:y:2013:i:3:p:271-284
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=100296

Keywords: Discrete data; Stochastic frontier analysis; Local maximum likelihood; Maximum simulated likelihood; Halton sequence; C01; C13; C14; C16; C25; C51;

Other versions of this item:

Find related papers by JEL classification:
  • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
  • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
  • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
  • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
  • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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Cited by:

  1. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.

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