Partial frontier efficiency analysis
Despite their frequent use in applied work, nonparametric approaches to efficiency analysis-namely, data envelopment analysis and free disposal hull- have bad reputations among econometricians. This is mainly because data envelopment analysis and free disposal hull represent deterministic approaches that are highly sensitive to outliers and measurement errors. However, so-called partial frontier approaches have recently been developed, namely, order-m and order-α. These approaches generalize free disposal hull by allowing for superefficient observations to be located beyond the estimated production-possibility frontier. Although these methods are also purely nonparametric, the sensitivity to outliers is substantially reduced by partial frontier approaches enveloping just a subsample of observations. In this article, I introduce the new Stata commands orderm and orderalpha, which implement order-m, order-α, and free disposal hull efficiency analysis in Stata. The commands allow for several options, such as statistical inference based on subsampling bootstrapping.
Volume (Year): 12 (2012)
Issue (Month): 3 (September)
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References listed on IDEAS
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- Anatoly Pilyavsky & Matthias Staat, 2008. "Efficiency and productivity change in Ukrainian health care," Journal of Productivity Analysis, Springer, vol. 29(2), pages 143-154, April.
- Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
- Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(02), pages 358-389, April.
- Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
- 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.
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