Estimation of Efficiency with the Stochastic Frontier Cost Function and Heteroscedasticity: A Monte Carlo Study
AbstractThe objective of this article is to address heteroscedasticity in the stochastic frontier cost function using aggregated data and verify it using a Monte Carlo study. We find that when the translog form of a stochastic frontier cost function with aggregated data is estimated, all explanatory variables can inversely affect the variation of error terms. Our Monte Carlo study shows that heteroscedasticity is only significant in the random effect and the unexplained error term not in the inefficiency error term. Also, it does not cause biases, which is quite opposite of previous research. These are because our model is approximately defined by first order Taylor series around zero inefficiency area. But, disregarding heteroscedasticity causes the average inefficiency to be overestimated when the variation of inefficiency term dominates the other error terms.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6408.
Date of creation: 2008
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-11-18 (All new papers)
- NEP-ECM-2008-11-18 (Econometrics)
- NEP-EFF-2008-11-18 (Efficiency & Productivity)
- NEP-ORE-2008-11-18 (Operations Research)
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- Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-11, January.
- Dickins, William T, 1990. "Error Components in Grouped Data: Is It Ever Worth Weighting?," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 328-33, May.
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