Nonparametric Efficiency Estimation in Stochastic Environments

Thierry Post; Laurens Cherchye; Timo Kuosmanen

doi:10.1287/opre.50.4.645.2854

Nonparametric Efficiency Estimation in Stochastic Environments

Thierry Post, Laurens Cherchye, Timo Kuosmanen

Not published at Aalto University

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

This paper develops a new nonparametric model for efficiency estimation. In contrast to Data Envelopment Analysis (DEA), it does not impose debatable production assumptions like free disposability and convexity, and it does not assume that the data are measured without error. The estimators are asymptotically unbiased and have an asymptotic variance that is comparable to that of stochastic frontier estimators (provided the latter use a correct specification of the functional form for the production relationships). In addition, the estimators can be computed using a simple enumeration algorithm.

Original language	English
Pages (from-to)	645-655
Journal	Operations Research
Volume	50
Issue number	4
DOIs	https://doi.org/10.1287/opre.50.4.645.2854
Publication status	Published - 2002
MoE publication type	A1 Journal article-refereed

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title = "Nonparametric Efficiency Estimation in Stochastic Environments",

abstract = "This paper develops a new nonparametric model for efficiency estimation. In contrast to Data Envelopment Analysis (DEA), it does not impose debatable production assumptions like free disposability and convexity, and it does not assume that the data are measured without error. The estimators are asymptotically unbiased and have an asymptotic variance that is comparable to that of stochastic frontier estimators (provided the latter use a correct specification of the functional form for the production relationships). In addition, the estimators can be computed using a simple enumeration algorithm.",

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AB - This paper develops a new nonparametric model for efficiency estimation. In contrast to Data Envelopment Analysis (DEA), it does not impose debatable production assumptions like free disposability and convexity, and it does not assume that the data are measured without error. The estimators are asymptotically unbiased and have an asymptotic variance that is comparable to that of stochastic frontier estimators (provided the latter use a correct specification of the functional form for the production relationships). In addition, the estimators can be computed using a simple enumeration algorithm.

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JO - Operations Research

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Nonparametric Efficiency Estimation in Stochastic Environments

Abstract

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