Robustness and inference in nonparametric partial-frontier modeling

Abdelaati Daouia () and Irène Gijbels ()
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Abdelaati Daouia: UT - Université de Toulouse
Irène Gijbels: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven

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Abstract: A major aim in recent nonparametric frontier modeling is to estimate a partial frontier well inside the sample of production units but near the optimal boundary. Two concepts of partial boundaries of the production set have been proposed: an expected maximum output frontier of order and a conditional quantile-type frontier of order . In this paper, we answer the important question of how the two families are linked. For each , we specify the order for which both partial production frontiers can be compared. We show that even one perturbation in data is sufficient for breakdown of the nonparametric order- frontiers, whereas the global robustness of the order- frontiers attains a higher breakdown value. Nevertheless, once the -frontiers break down, they become less resistant to outliers than the order- frontiers. Moreover, the -frontiers have the advantage to be statistically more efficient. Based on these findings, we suggest a methodology for identifying outlying data points. We establish some asymptotic results, contributing to important gaps in the literature. The theoretical findings are illustrated via simulations and real data.

Keywords: C13; C14; D20; Asymptotics; Breakdown values; Econometric frontiers; Outliers detection (search for similar items in EconPapers)
Date: 2011-03-05
Note: View the original document on HAL open archive server: https://hal.science/hal-00796744
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Citations: View citations in EconPapers (48)

Published in Econometrics, 2011, 161 (2), pp.147. ⟨10.1016/j.jeconom.2010.12.002⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00796744

DOI: 10.1016/j.jeconom.2010.12.002

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