Nonparametric efficiency estimation in stochastic environments
成果类型：
Article
署名作者：
Post, T; Cherchye, L; Kuosmanen, T
署名单位：
Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; KU Leuven
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.50.4.645.2854
发表日期：
2002
页码：
645-655
关键词：
摘要：
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.