IDEAS home Printed from https://ideas.repec.org/p/sgo/wpaper/1403.html
   My bibliography  Save this paper

Stochastic Frontier Models Using GAUSS

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://tinyurl.com/2xa4nmw5
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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, December.
    2. 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.
    3. 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.
    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. 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.
    6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    2. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    3. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    4. Young Hoon Lee, 2009. "Frontier Models and their Application to the Sports Industry," Working Papers 0903, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised 2009.
    5. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    6. 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.
    7. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    8. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    9. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    10. Coelli, Tim J., 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 1-27, December.
    11. Massimiliano Piacenza, 2002. "Regulatory Constraints and Cost Efficiency of the Italian Public Transit Systems: An Exploratory Stochastic Frontier Model," CERIS Working Paper 200202, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    12. Karagiannis, Giannis & Tzouvelekas, Vangelis, 2009. "Parametric Measurement of Time-Varying Technical Inefficiency: Results from Competing Models," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 10(1), pages 1-30.
    13. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    14. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    15. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    16. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    17. William Greene, 2004. "Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 959-980, October.
    18. Gian Carlo Scarsi, 1999. "Local Electricity Distribution in Italy: Comparative Efficiency Analysis and Methodological Cross-Checking," Working Papers 1999.16, Fondazione Eni Enrico Mattei.
    19. Giannis Karagiannis & Vangelis Tzouvelekas, 1999. "Measuring Technical Efficiency with Panel Data: Results from Competing Models," Working Papers 9914, University of Crete, Department of Economics.
    20. Marc Rohde & Christoph Breuer, 2018. "Competing by investments or efficiency? Exploring financial and sporting efficiency of club ownership structures in European football," Sport Management Review, Taylor & Francis Journals, vol. 21(5), pages 563-581, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sgo:wpaper:1403. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jung Hur (email available below). General contact details of provider: https://edirc.repec.org/data/risogkr.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.