Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroscedasticity

被引：0

作者：

Auburn Univ, Auburn, United States ^{[1
]}

机构：

来源：

Eur J Oper Res | / 1卷 / 140-148期

关键词：

Corrected ordinary least squares - Data envelopment analysis - Heteroscedasticity - Maximum likelihood - Small sample properties;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The purpose of this paper is to examine the small sample properties of maximum likelihood (ML), corrected ordinary least squares (COLS), and data envelopment analysis (DEA) estimators of the parameters in frontier models in the presence of heteroscedasticity in the two-sided, or measurement, error term. Using Monte Carlo methods, we find that heteroscedasticity in the two-sided error term introduces substantial biases into ML, COLS, and DEA estimators. Although none of the estimators perform well, both ML and COLS are found to be superior to DEA in the presence of heteroscedasticity in the two-sided error.

引用