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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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  • Wen-Jen Tsay & Cliff J. Huang & Tsu-Tan Fu & I-Lin Ho, 2009. "Maximum Likelihood Estimation of Censored Stochastic Frontier Models: An Application to the Three-Stage DEA Method," IEAS Working Paper : academic research 09-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:09-a003
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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.

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    Keywords

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