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A dynamic stochastic frontier production model with time-varying efficiency

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Author Info
Evangelia Desli
Subhash C. Ray
Subal C. Kumbhakar

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Abstract

In this paper technical efficiency is introduced via the intercept that evolve over time as a AR(1) process in a stochastic frontier (SF) framework in a panel data framework. Following are the distinguishing features of the model. First, the model is dynamic in nature. Second, it can separate technical inefficiency from fixed firm-specific effects which are not part of inefficiency. Third, the model allows estimation of technical change separate from change in technical efficiency. It is proposed that the ML method be used estimate the parameters of the model. Finally, expressions are derived to calculate/predict technical inefficiency (efficiency).

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Article provided by Taylor and Francis Journals in its journal Applied Economics Letters.

Volume (Year): 10 (2003)
Issue (Month): 10 (August)
Pages: 623-626
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Handle: RePEc:taf:apeclt:v:10:y:2003:i:10:p:623-626

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. Kumbhakar, Subal C., 1987. "The specification of technical and allocative inefficiency in stochastic production and profit frontiers," Journal of Econometrics, Elsevier, vol. 34(3), pages 335-348, March. [Downloadable!] (restricted)
  3. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211. [Downloadable!] (restricted)
  4. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
  6. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July. [Downloadable!] (restricted)
  7. Cooley, Thomas F & Prescott, Edward C, 1973. "An Adaptive Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 364-71, June. [Downloadable!] (restricted)
  8. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August. [Downloadable!] (restricted)
  9. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  10. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200. [Downloadable!] (restricted)
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  1. David I. Stern, 2004. "Diffusion of Emissions Abating Technology," Rensselaer Working Papers in Economics 0420, Rensselaer Polytechnic Institute, Department of Economics. [Downloadable!]
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