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Stochastic frontier models

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  • Wang, Hung-Jen

Abstract

The stochastic frontier model was first proposed in the context of production function estimation to account for the effect of technical inefficiency. The inefficiency causes actual output to fall below the potential level (that is, the production frontier) and also raises production cost above the minimum level (that is, the cost frontier). Recent applications of the model are found in many fields of study including labour, finance, and economic growth. In these applications, the observed outcome (of wages, investment, and so on) is modelled as being deviating from a frontier level in one direction owing to factors such as information asymmetry.

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  • Wang, Hung-Jen, 2006. "Stochastic frontier models," MPRA Paper 31079, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:31079
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    References listed on IDEAS

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    1. Schmidt, Peter & Lin, Tsai-Fen, 1984. "Simple tests of alternative specifications in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 24(3), pages 349-361, March.
    2. Kumbhakar, Subal C. & Wang, Hung-Jen, 2006. "Estimation of technical and allocative inefficiency: A primal system approach," Journal of Econometrics, Elsevier, vol. 134(2), pages 419-440, October.
    3. Hunt-McCool, Janet & Koh, Samuel C & Francis, Bill B, 1996. "Testing for Deliberate Underpricing in the IPO Premarket: A Stochastic Frontier Approach," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1251-1269.
    4. Meeusen, Wim & van den Broeck, J, 1977. "Technical Efficiency and Dimension of the Firm: Some Results on the Use of Frontier Production Functions," Empirical Economics, Springer, vol. 2(2), pages 109-122.
    5. Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, vol. 88(3), pages 300-305, September.
    6. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "The Joint Measurement of Technical and Allocative Inefficiencies: An Application of Bayesian Inference in Nonlinear Random-Effects Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 736-747, September.
    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. 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.
    9. Greene, William H., 1980. "On the estimation of a flexible frontier production model," Journal of Econometrics, Elsevier, vol. 13(1), pages 101-115, May.
    10. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    11. Kumbhakar, Subal C., 1997. "Modeling allocative inefficiency in a translog cost function and cost share equations: An exact relationship," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 351-356.
    12. 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.
    13. Hofler, Richard A & Murphy, Kevin J, 1992. "Underpaid and Overworked: Measuring the Effect of Imperfect Information on Wages," Economic Inquiry, Western Economic Association International, vol. 30(3), pages 511-529, July.
    14. Wang, Hung-Jen, 2003. "A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 406-419, July.
    15. 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)

    Citations

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    Cited by:

    1. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    2. Tabak, Benjamin M. & Miranda, Rogério Boueri & Fazio, Dimas M., 2013. "A geographically weighted approach to measuring efficiency in panel data: The case of US saving banks," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3747-3756.
    3. 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.
    4. 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.
    5. Tai-Hsin Huang & Mei-Hui Wang, 2004. "Estimation of scale and scope economies in multiproduct banking: evidence from the Fourier flexible functional form with panel data," Applied Economics, Taylor & Francis Journals, vol. 36(11), pages 1245-1253.
    6. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    7. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    8. Thomas Triebs & Subal C. Kumbhakar, 2012. "Management Practice in Production," ifo Working Paper Series 129, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Ma Ángeles Díaz & Rosario Sánchez, 2011. "Gender and potential wage in Europe: a stochastic frontier approach," International Journal of Manpower, Emerald Group Publishing Limited, vol. 32(4), pages 410-425, July.
    10. Subal Kumbhakar & Raquel Ortega-Argilés & Lesley Potters & Marco Vivarelli & Peter Voigt, 2012. "Corporate R&D and firm efficiency: evidence from Europe’s top R&D investors," Journal of Productivity Analysis, Springer, vol. 37(2), pages 125-140, April.
    11. Wei Wang & Christine Amsler & Peter Schmidt, 2011. "Goodness of fit tests in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 35(2), pages 95-118, April.
    12. Martin Falk, 2009. "Are multi-resort ski conglomerates more efficient?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 529-538.
    13. Jaenicke, Edward C. & Frechette, Darren L. & Larson, James A., 2003. "Estimating Production Risk and Inefficiency Simultaneously: An Application to Cotton Cropping Systems," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(3), pages 1-18, December.
    14. Tsionas, Efthymios & Kumbhakar, Subal, 2006. "Estimation of Technical and Allocative Inefficiencies in a Cost System: An Exact Maximum Likelihood Approach," MPRA Paper 20173, University Library of Munich, Germany.

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    More about this item

    Keywords

    aftermarkets; allocative inefficiency; convergence; copulas; cost functions; duality; financing constraints; fixed-effect panel estimators; labour market search models; likelihood functions; nonparametric estimation; production function estimation; production functions; semiparametric estimation; stochastic cost frontiers; stochastic frontier models; technical inefficiency; technological catch-up; truncated distributions;
    All these keywords.

    JEL classification:

    • 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
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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