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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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Bibliographic Info

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

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Web page: http://www.springerlink.com/link.asp?id=100296

Related research

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

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  1. Gozalo, Pedro & Linton, Oliver, 2000. "Local nonlinear least squares: Using parametric information in nonparametric regression," Journal of Econometrics, Elsevier, vol. 99(1), pages 63-106, November.
  2. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
  3. William H. Greene, 2000. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," Working Papers 00-05, New York University, Leonard N. Stern School of Business, Department of Economics.
  4. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
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  9. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, April.
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  11. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
  12. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  13. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-23, May-June.
  14. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
  15. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
  16. Wei Siang Wang & Peter Schmidt, 2007. "On The Distribution of Estimated Technical Efficiency in Stochastic Frontier Models," CEPA Working Papers Series WP022007, School of Economics, University of Queensland, Australia.
  17. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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  19. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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