Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroscedasticity
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 t...
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| Veröffentlicht in: | European journal of operational research 1998-07, Vol.108 (1), p.140-148 |
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| container_title | European journal of operational research |
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| creator | Bojani, Antonio N. Caudill, Steven B. Ford, Jon M. |
| description | 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. |
| doi_str_mv | 10.1016/S0377-2217(97)00101-X |
| format | Article |
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Using Monte Carlo methods, we find that heteroscedasticity in the two-sided error term introduces substantial biases into ML, COLS, and DEA estimators. 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| fulltext | fulltext |
| identifier | ISSN: 0377-2217 |
| ispartof | European journal of operational research, 1998-07, Vol.108 (1), p.140-148 |
| issn | 0377-2217 1872-6860 |
| language | eng |
| recordid | cdi_proquest_journals_204135010 |
| source | RePEc; ScienceDirect Journals (5 years ago - present) |
| subjects | Data envelopment analysis Exact sciences and technology Linear inference, regression Mathematical models Mathematics Maximum strategies Monte Carlo simulation Operations research Probability and statistics Regression Sciences and techniques of general use Statistics Stochastic frontier Studies |
| title | Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroscedasticity |
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