Oriented stochastic data envelopment models: Ranking comparison to stochastic frontier approach
Results of data envelopment analysis sensitively respond to stochastic noise in the data. In this paper, by introduction of output augmentation and input reduction I extend additive models for stochastic data envelopment analysis (SDEA), which were developed by Li (1998) to handle the noise in the data. Applying the linearization procedure by Li (1998) the linearized versions of models are derived. In the empirical part of this work, the effi- ciency scores of Indonesian rice farms are computed. The computed scores are compared to the stochastic frontier approach scores by Druska and Horrace (2004) and weak ranking consistency with results of stochastic frontier method is observed.
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- M. Halme & P. Korhonen, 1998.
"Restricting Weights in Value Efficiency Analysis,"
ir98104, International Institute for Applied Systems Analysis.
- William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
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- Abdul Wadud & Ben White, 2000. "Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods," Applied Economics, Taylor & Francis Journals, vol. 32(13), pages 1665-1673.
- Viliam Druska & William C. Horrace, 2004.
"Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming,"
American Journal of Agricultural Economics,
Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
- Viliam Druska & William C. Horrace, 2002. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," Econometrics 0206004, EconWPA, revised 11 May 2003.
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