Heteroscedasticity in Stochastic Frontier Models: a Monte Carlo Analysis
AbstractThis paper uses Monte Carlo experimentation to investigate the finite sample properties of the maximum likelihood (ML) estimators of the half-normal stochastic frontier production functions in the presence of heteroscedasticity. It is found that when heteroscedasticity exists correcting for it leads not only to a substantial improvement of the statistical properties of estimators but also to improved efficiency and ranking measures. On the other hand correcting for heteroscedasticity when there is none has serious adverse results. Hence, there is a need for testing for heteroscedasticity and if there is any the appropriate correction should be made.
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Bibliographic InfoPaper provided by Exeter University, Department of Economics in its series Discussion Papers with number 9914.
Length: 18 pages
Date of creation: 1999
Date of revision:
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STATISTICAL ANALYSIS ; ECONOMETRICS ; ENTERPRISES;
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- Kaddour Hadri & Julie Whittaker, 1999.
"Efficiency, Environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers,"
Journal of Applied Economics,
Universidad del CEMA, vol. 0, pages 337-356, November.
- Hadri, K. & Whittaker, J., 1995. "Efficiency, environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers," Discussion Papers 9505, Exeter University, Department of Economics.
- Tiziana Laureti, 2008. "Modelling Exogenous Variables in Human Capital Formation through a Heteroscedastic Stochastic Frontier," International Advances in Economic Research, Springer, vol. 14(1), pages 76-89, February.
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