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Estimating complex production functions: The importance of starting values

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  • Neal, Mark

Abstract

Production functions that take into account uncertainty can be empirically estimated by taking a state contingent view of the world. Where there is no a priori information to allocate data amongst a small number of states, the estimation may be carried out with finite mixtures model. The complexity of the estimation almost guarantees a large number of local maxima for the likelihood function. However, it is shown, with examples, that a variation on the traditional method of finding starting values substantially improves the estimation results. One of the major benefits of the proposed method is the reliable estimation of a decision maker's ability to substitute output between states, justifying a preference for the state contingent approach over the use of a stochastic production function.

Suggested Citation

  • Neal, Mark, 2007. "Estimating complex production functions: The importance of starting values," Risk and Sustainable Management Group Working Papers 151178, University of Queensland, School of Economics.
  • Handle: RePEc:ags:uqsers:151178
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    File URL: http://purl.umn.edu/151178
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    References listed on IDEAS

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    1. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    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. White, Halbert, 1980. "Using Least Squares to Approximate Unknown Regression Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 149-170, February.
    5. repec:tcd:wpaper:tep4 is not listed on IDEAS
    6. Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
    7. Tonsor, Glynn T. & Kastens, Terry L., 2006. "How Much Do Starting Values Really Matter? An Empirical Comparison of Genetic Algorithm and Traditional Approaches," 2006 Annual meeting, July 23-26, Long Beach, CA 21252, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Production function; econometrics; starting values; state contingent production; Production Economics; Production Economics; Risk and Uncertainty; C51; D24;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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