IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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

Author Info

Listed author(s):
  • 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

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://hdl.handle.net/10.1007/s11123-012-0286-y
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Springer in its journal Journal of Productivity Analysis.

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

as
in new window

Handle: RePEc:kap:jproda:v:39:y:2013:i:3:p:271-284
DOI: 10.1007/s11123-012-0286-y
Contact details of provider: Web page: http://www.springer.com

Order Information: Web: http://www.springer.com/economics/microeconomics/journal/11123/PS2

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
  • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. Martins-Filho, Carlos & Yao, Feng, 2007. "Nonparametric frontier estimation via local linear regression," Journal of Econometrics, Elsevier, vol. 141(1), pages 283-319, November.
  2. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
  3. Bronwyn H. Hall & Clint Cumminq & Elizabeth S. Laderman & Joy Mundy, 1988. "The R&D Master File Documentation," NBER Technical Working Papers 0072, National Bureau of Economic Research, Inc.
  4. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521477444, January.
  5. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711.
  6. Park, B. U. & Sickles, R. C. & Simar, L., 1998. "Stochastic panel frontiers: A semiparametric approach," Journal of Econometrics, Elsevier, vol. 84(2), pages 273-301, June.
  7. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
  8. William Greene, 2003. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," Journal of Productivity Analysis, Springer, vol. 19(2), pages 179-190, April.
  9. repec:fth:harver:1473 is not listed on IDEAS
  10. Wang, Wei Siang & Schmidt, Peter, 2009. "On the distribution of estimated technical efficiency in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 148(1), pages 36-45, January.
  11. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
  12. 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.
  13. 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.
  14. Pakes, Ariel S, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," Econometrica, Econometric Society, vol. 54(4), pages 755-784, July.
  15. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2008. "Local likelihood estimation of truncated regression and its partial derivatives: Theory and application," Journal of Econometrics, Elsevier, vol. 146(1), pages 185-198, September.
  16. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800 Elsevier.
  17. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2007. "Semiparametric efficient estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 136(1), pages 281-301, January.
  18. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
  19. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-764, May.
  20. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
  21. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
  22. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
  23. Orme, Chris, 1990. "The small-sample performance of the information-matrix test," Journal of Econometrics, Elsevier, vol. 46(3), pages 309-331, December.
  24. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
  25. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  26. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
  27. 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.
  28. Pakes, Ariel & Griliches, Zvi, 1980. "Patents and R&D at the firm level: A first report," Economics Letters, Elsevier, vol. 5(4), pages 377-381.
  29. 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.
  30. John Xu Zheng, 1996. "A consistent test of functional form via nonparametric estimation techniques," Journal of Econometrics, Elsevier, vol. 75(2), pages 263-289, December.
  31. 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.
  32. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
  33. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
  34. 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.
  35. 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.
  36. Sickles, Robin C., 2005. "Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings," Journal of Econometrics, Elsevier, vol. 126(2), pages 305-334, June.
  37. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, January.
  38. 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 203-223, May-June.
Full references (including those not matched with items on IDEAS)

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

as
in new window


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.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Access and download statistics

When requesting a correction, please mention this item's handle: RePEc:kap:jproda:v:39:y:2013:i:3:p:271-284. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)

or (Rebekah McClure)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.