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Estimation of Inefficiency using a Firm-specific Frontier Model

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  • Das, Arabinda
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    Abstract

    It has been argued that the deterministic frontier approach in inefficiency measurement has a major limitation as inefficiency is mixed with measurement error (statistical noise) in this approach. The result is that inefficiency is contaminated with noise. Later stochastic frontier approach improves the situation with allowing a statistical noise in the model which captures all other factors other than inefficiency. The stochastic frontier model has been used for inefficiency analysis despite its complicated form and estimation procedure. This paper introduced an extra parameter which estimates the amount of proportion that an error component shares in the observational error. An EM estimation approach is used for estimation of the model and a test procedure is developed to test the significance of presence of the error component in the observational error.

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    File URL: http://mpra.ub.uni-muenchen.de/46168/
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 46168.

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    Date of creation: 13 Apr 2013
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    Handle: RePEc:pra:mprapa:46168

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    Keywords: stochastic frontier model; skew-normal distribution; identification; EM algorithm; Monte Carlo simulation.;

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    1. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
    2. 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.
    3. Richmond, J, 1974. "Estimating the Efficiency of Production," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 515-21, June.
    4. Greene, William H., 1980. "On the estimation of a flexible frontier production model," Journal of Econometrics, Elsevier, vol. 13(1), pages 101-115, May.
    5. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(03), December.
    6. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    7. 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.
    8. Ruggiero, John, 1999. "Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 555-563, June.
    9. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    10. Manoranjan Pal, 2004. "A Note on a Unified Approach to the Frontier Production Function Models With Correlated Non-Normal Error Components: The Case of Cross Section Data," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 39(1), pages 7-18, January.
    11. 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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