Stochastic Frontier Models with Threshold Efficiency
AbstractThis paper proposes a tail-truncated stochastic frontier model that allows for the truncation of technical efficiency from below. The truncation bound implies the inefficiency threshold for survival and also can be used as a measure of market competition. Specifically, this paper assumes a uniform distribution of technical inefficiency and derives the likelihood function. Even though this distributional assumption imposes a strong restriction that technical inefficiency has a uniform probability density over [0,], where is the threshold parameter, this model has two advantages: (i) the reduction of the number of parameters from the more complicated tail-truncated models allows better performance in numerical optimization; and (ii) the threshold parameter itself represents a degree of competition because the variance of technical inefficiency is dependent solely on the parameter. The Monte Carlo simulation results support the argument that this model approximates the distribution of inefficiency precisely.
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Bibliographic InfoPaper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 1205.
Length: 23 pages
Date of creation: 2011
Date of revision:
Stochastic frontier; technical efficiency; threshold inefficiency; uniform distribution; productivity distribution;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
- L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
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- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- Qu Feng & William C. Horrace, 2010.
"Alternative Technical Efficiency Measures: Skew, Bias, and Scale,"
Center for Policy Research Working Papers
121, Center for Policy Research, Maxwell School, Syracuse University.
- Qu Feng & William C. Horrace, 2012. "Alternative technical efficiency measures: Skew, bias and scale," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 253-268, 03.
- Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
- Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
- Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
- Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
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