Title: Robustness and inference in nonparametric partial frontier modeling
Authors: Daouia, Abdelaati ×
Gijbels, Irène #
Issue Date: 2011
Series Title: Journal of Econometrics vol:161 issue:2 pages:147-165
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 m=1,2,... and a conditional quantile-type frontier of order [alpha][set membership, variant]]0,1]. In this paper, we answer the important question of how the two families are linked. For each m, we specify the order [alpha] 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-m frontiers, whereas the global robustness of the order-[alpha] frontiers attains a higher breakdown value. Nevertheless, once the [alpha] frontiers break down, they become less resistant to outliers than the order-m frontiers. Moreover, the m 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.
ISSN: 0304-4076
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

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