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Stochastic Frontier Models Using GAUSS

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  • Young H. Lee

    (Department of Economics, Sogang University, Seoul)

Abstract

This paper discusses the use of ten different GAUSS programs for various stochastic frontier models. SFM_MLE_cross-section provides maximum likelihood estimates (MLE) for four different stochastic frontier models with cross-sectional data: those of Aigner, Lovell, and Schmidt (1977), Stevenson (1980), Almanidis, Qian, and Sickles (2014), and Lee and Lee (2014). There are two programs for panel data stochastic frontier models with the time-invariant efficiency assumption. SFM_BC88_MLE provides the MLE of Battese and Coelli (1988) and SFM_SS presents the within and generalized least squared estimates of Schmidt and Sickles (1984). Finally, seven programs allow the use of different stochastic frontier models with time-varying efficiency: SFM_BC92 for Battese and Coelli (1992), SFM_Kum for Kumbhakar (1991), SFM_CSS for Cornwell, Schmidt, and Sickles (1990), SFM_LS for Lee and Schmidt (1993), SFM_GrLS for Lee (2006), SFM_GrBC for Lee (2010), and SFM_ALS07 for Ahn, Lee, and Schmidt (2007). A noteworthy feature is that all seven programs estimate production function parameters by adopting the fixed effect treatment.

Suggested Citation

  • Young H. Lee, 2014. "Stochastic Frontier Models Using GAUSS," Working Papers 1403, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    • Handle: RePEc:sgo:wpaper:1403
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    References listed on IDEAS

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    1. Lee, Young Hoon, 2006. "A stochastic production frontier model with group-specific temporal variation in technical efficiency," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1616-1630, November.
    2. Robin C. Sickles & William C. Horrace (ed.), 2014. "Festschrift in Honor of Peter Schmidt," Springer Books, Springer, edition 127, number 978-1-4899-8008-3, June.
    3. 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.
    4. 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.
    5. Lee, Young Hoon, 2010. "Group-specific stochastic production frontier models with parametric specifications," European Journal of Operational Research, Elsevier, vol. 200(2), pages 508-517, January.
    6. 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.
    7. 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.
    8. Seung Ahn & Young Lee & Peter Schmidt, 2007. "Stochastic frontier models with multiple time-varying individual effects," Journal of Productivity Analysis, Springer, vol. 27(1), pages 1-12, February.
    9. 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.
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