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Estimation and Inference in Nonparametric Frontier Models provides a thorough examination of this topic for students and researchers alike. While nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, it is often done without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators and methods for making statistical inference has established a link between frontier estimation and extreme value theory. New estimators that avoid many of the problems inherent with traditional efficiency estimators have been developed and these new estimators are robust with respect to outliers and avoid the problem of dimensionality. In addition, statistical properties, including asymptotic distributions, of the new estimators are uncovered. Finally, the authors show several approaches for introducing environmental variables into production models.
List of contents
Nonparametric Statistical Models of Production: Combining Economics and Statistics. The Nonparametric Envelopment Estimators. Bootstrap Inference using DEA and FDH Estimators. Robust Order-m Estimators. Robust Order-? Estimators. Outlier Detection. Explaining Inefficiency. Unanswered Questions, Promising Ideas. Acknowledgements. References.
