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On the Consistency of Maximum Likelihood Estimation of Monotone and Concave Production Frontiers

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  • Bharat Sarath
  • Ajay Maindiratta

Abstract

Banker and Maindiratta (1992) provides a method for the estimation of a stochastic production frontier from the class of all monotone and concave functions. A key aspect of their procedure is that the arguments in the log-likelihood function are the fitted frontier outputs themselves rather than the parameters of some assumed parametric functional form. Estimation from the desired class of functions is ensured by constraining the fitted points to lie on some monotone and concave surface via a set of inequality restrictions. In this paper, we establish that this procedure yields consistent estimates of the fitted outputs and the composed error density function parameters. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Bharat Sarath & Ajay Maindiratta, 1997. "On the Consistency of Maximum Likelihood Estimation of Monotone and Concave Production Frontiers," Journal of Productivity Analysis, Springer, vol. 8(3), pages 239-246, August.
    • Handle: RePEc:kap:jproda:v:8:y:1997:i:3:p:239-246
    DOI: 10.1023/A:1007725103835
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    References listed on IDEAS

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    1. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    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 435-444, June.
    3. 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.
    4. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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    Cited by:

    1. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    2. Rajiv Banker & Surya Janakiraman & Ram Natarajan, 2002. "Evaluating the Adequacy of Parametric Functional Forms in Estimating Monotone and Concave Production Functions," Journal of Productivity Analysis, Springer, vol. 17(1), pages 111-132, January.
    3. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.

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