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A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency

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  • Evangelia Desli

    (Lloyds of London)

  • Subhash C. Ray

    (University of Connecticut)

  • Subal C. Kumbhakar

    (SUNY Binghampton)

Abstract

In this paper we introduce technical efficiency 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 one to estimate technical change separate from change in technical efficiency. We propose the ML method to estimate the parameters of the model. Finally, we derive expressions to calculate/predict technical inefficiency (efficiency).

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2003-15.

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Length: 11 pages
Date of creation: Sep 2002
Date of revision:
Handle: RePEc:uct:uconnp:2003-15

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  1. 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.
  2. 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.
  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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  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.
  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. 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.
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Cited by:
  1. Weaver, Robert D. & Curtiss, Jarmila & Brummer, Bernhard, 2005. "Technical Efficiency Effects of Technological Change: Another Perspective on GM Crops," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24528, European Association of Agricultural Economists.
  2. Auci, Sabrina & Castelli, Annalisa, 2011. "Pollution and economic growth: a maximum likelihood estimation of environmental Kuznets curve," MPRA Paper 53441, University Library of Munich, Germany.
  3. Mundula, Luigi & Auci, Sabrina, 2013. "Smart Cities and a Stochastic Frontier Analysis: A Comparison among European Cities," MPRA Paper 51586, University Library of Munich, Germany.
  4. Sauer, Johannes & Graversen, Jesper T. & Park, Timothy A., 2006. "Breathtaking or Stagnating? - Productivity, Technical Change and Structural Dynamics in Danish Organic Farming," 2006 Annual meeting, July 23-26, Long Beach, CA 21481, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  5. David I. Stern, 2004. "Diffusion of Emissions Abating Technology," Rensselaer Working Papers in Economics 0420, Rensselaer Polytechnic Institute, Department of Economics.
  6. Shaik, Saleem & Allen, Albert J. & Myles, Albert E. & Yeboah, Osei-Agyeman, 2008. "Importance of Financial Variables on Efficiency of Class I Railroads in the United States," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6874, Southern Agricultural Economics Association.
  7. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2013. "Does cutting back the public sector improve efficiency? Some evidence from 15 European countries," CEIS Research Paper 274, Tor Vergata University, CEIS, revised 30 Apr 2013.
  8. A. Peyrache & A. N. Rambaldi, 2012. "A State-Space Stochastic Frontier Panel Data Model," CEPA Working Papers Series WP012012, School of Economics, University of Queensland, Australia.

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