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Inference by the m out of n bootstrap in nonparametric frontier models

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Simar, Léopold [UCL] Wilson, Paul W. [Clemson University, Clemson, South Carolina, USA]

It is well-known that the naive bootstrap yields inconsistent inference in the context of data envelopment analysis (DEA) or free disposal hull (FDH) estimators in nonparametric frontier models. For inference about efficiency of a single, fixed point, drawing bootstrap pseudo-samples of size m < n provides consistent inference, although coverages are quite sensitive to the choice of subsample size m. We provide a probabilistic framework in which these methods are shown to valid for statistics comprised of functions of DEA or FDH estimators. We examine a simple, data-based rule for selecting m suggested by Politis et al. (Stat Sin 11:1105–1124, 2001), and provide Monte Carlo evidence on the size and power of our tests. Our methods (i) allow for heterogeneity in the inefficiency process, and unlike previous methods, (ii) do not require multivariate kernel smoothing, and (iii) avoid the need for solutions of intermediate linear programs.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2011
Language Anglais
Journal information "Journal of Productivity Analysis" - Vol. 36, no. 1, p. 33-53 (2011)
Peer reviewed yes
Publisher Springer New York LLC ((United States) New York)
issn 0895-562X
e-issn 1573-0441
Publication status Publié
Affiliations UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Clemson University, Clemson, South Carolina, USA - The John E. Walker Department of Economics
Links
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Bibliographic reference Simar, Léopold ; Wilson, Paul W.. Inference by the m out of n bootstrap in nonparametric frontier models. In: Journal of Productivity Analysis, Vol. 36, no. 1, p. 33-53 (2011)
Permanent URL http://hdl.handle.net/2078.1/95927