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Maximum Likelihood Estimation of Censored Stochastic Frontier Models: An Application to the Three-Stage DEA Method

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Abstract

This paper takes issues with the appropriateness of applying the stochastic frontier analysis (SFA) technique to account for environmental effects and statistical noise in the popular three-stage data envelopment analysis (DEA). A correctly specified SFA model with a censored dependent variable and the associated maximum likelihood estimation (MLE) are proposed. The simulations show that the finite sample performance of the proposed MLE of the censored SFA model is very promising. An empirical example of farmers’ credit unions in Taiwan illustrates the comparison between the censored and standard SFA in accounting for environmental effects and statistical noise.

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Bibliographic Info

Paper provided by Institute of Economics, Academia Sinica, Taipei, Taiwan in its series IEAS Working Paper : academic research with number 09-A003.

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Length: 27 pages
Date of creation: Mar 2009
Date of revision:
Handle: RePEc:sin:wpaper:09-a003

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Keywords: Three-stage data envelopment analysis; stochastic frontier analysis; censored stochastic frontier model;

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References

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  1. Leigh M. Drake & Richard Simper, 2005. "The Measurement Of Police Force Efficiency: An Assessment Of U.K. Home Office Policy," Contemporary Economic Policy, Western Economic Association International, vol. 23(4), pages 465-482, October.
  2. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
  3. Beniamina Buzzo Margari & Fabrizio Erbetta & Carmelo Petraglia & Massimiliano Piacenza, 2006. "Regulatory and Environmental Effects on Public Transit Efficiency. A Mixed DEA-SFA Approach," CERIS Working Paper 200613, Institute for Economic Research on Firms and Growth - Moncalieri (TO).
  4. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
  5. 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.
  6. Liu, Junming & Tone, Kaoru, 2008. "A multistage method to measure efficiency and its application to Japanese banking industry," Socio-Economic Planning Sciences, Elsevier, vol. 42(2), pages 75-91, June.
  7. 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.
  8. Jose Manuel Cordero-Ferrera & Francisco Pedraja-Chaparro & Javier Salinas-Jimenez, 2008. "Measuring efficiency in education: an analysis of different approaches for incorporating non-discretionary inputs," Applied Economics, Taylor & Francis Journals, vol. 40(10), pages 1323-1339.
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Cited by:
  1. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2011. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Working Papers 11002, Concordia University, Department of Economics.

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