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A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency

Author

Listed:
  • 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).

Suggested Citation

  • Evangelia Desli & Subhash C. Ray & Subal C. Kumbhakar, 2002. "A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency," Working papers 2003-15, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:2003-15
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    References listed on IDEAS

    as
    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.
    2. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    3. 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 435-444, June.
    4. 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 364-371, June.
    5. 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.
    6. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, pages 43-64.
    7. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    8. 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.
    9. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1989. "Production Frontiers With Cross-Sectinal And Time-Series Variation In Efficiency Levels," Working Papers 89-18, C.V. Starr Center for Applied Economics, New York University.
    10. 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.
    11. 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.
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    Citations

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    Cited by:

    1. 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.
    2. A. Peyrache & A. N. Rambaldi, 2017. "Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS," Journal of Productivity Analysis, Springer, vol. 47(2), pages 143-166, April.
    3. Duygun, Meryem & Kutlu, Levent & Sickles, Robin C., 2014. "Measuring Productivity and Efficiency: A Kalman," Working Papers 15-010, Rice University, Department of Economics.
    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. Meryem Duygun & Levent Kutlu & Robin C. Sickles, 2016. "Measuring productivity and efficiency: a Kalman filter approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 155-167, December.
    6. Weaver, Robert D. & Curtiss, Jarmila & Brümmer, 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.
    7. repec:eee:ejores:v:263:y:2017:i:3:p:1078-1094 is not listed on IDEAS
    8. 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.
    9. 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.
    10. 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.
    11. James D. Adams, 2005. "Industrial R&D Laboratories: Windows on Black Boxes?," The Journal of Technology Transfer, Springer, vol. 30(2_2), pages 129-137, January.
    12. Kutlu, Levent, 2017. "A constrained state space approach for estimating firm efficiency," Economics Letters, Elsevier, vol. 152(C), pages 54-56.
    13. 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.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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