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Stochastic frontier analysis using Stata

Author

Listed:
  • Federico Belotti

    () (University of Rome Tor Vergata)

  • Silvio Daidone

    () (University of York)

  • Giuseppe Ilardi

    () (Economic and Financial Statistics Department, Bank of Italy)

  • Vincenzo Atella

    () (University of Rome Tor Vergata)

Abstract

This article describes sfcross and sfpanel, two new Stata commands for the estimation of cross-sectional and panel-data stochastic frontier models. sfcross extends the capabilities of the frontier command by including additional models (Greene, 2003, Journal of Productivity Analysis 19: 179–190; Wang, 2002, Journal of Productivity Analysis 18: 241–253) and command functionalities, such as the possibility of managing complex survey data characteristics. Similarly, sfpanel allows one to fit a much wider range of time-varying inefficiency models compared with the xtfrontier command, including the model of Cornwell, Schmidt, and Sickles (1990, Journal of Econometrics 46: 185–200); the model of Lee and Schmidt (1993, in The Measurement of Productive Efficiency: Techniques and Applications), a production frontier model with flexible temporal variation in technical efficiency; the flexible model of Kumbhakar (1990, Journal of Econometrics 46: 201–211); the inefficiency effects model of Battese and Coelli (1995 Empirical Economics 20: 325–332); and the "true" fixed- and random-effects models of Greene (2005a, Journal of Econometrics 126: 269–303). A brief overview of the stochastic frontier literature, a description of the two commands and their options, and examples using simulated and real data are provided. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
  • Handle: RePEc:tsj:stataj:v:13:y:2013:i:4:p:718-758
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    References listed on IDEAS

    as
    1. David M. Drukker & Richard Gates, 2006. "Generating Halton sequences using Mata," Stata Journal, StataCorp LP, vol. 6(2), pages 214-228, June.
    2. Han, Chirok & Orea, Luis & Schmidt, Peter, 2005. "Estimation of a panel data model with parametric temporal variation in individual effects," Journal of Econometrics, Elsevier, vol. 126(2), pages 241-267, June.
    3. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    4. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    5. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    6. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    7. 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.
    8. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
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    17. Federico Belotti & Giuseppe Ilardi, 2012. "Consistent Estimation of the “True” Fixed-effects Stochastic Frontier Model," CEIS Research Paper 231, Tor Vergata University, CEIS, revised 18 Apr 2012.
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    More about this item

    Keywords

    sfcross; sfpanel; stochastic frontier analysis; production frontier; cost frontier; cross-sectional; panel data;

    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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