Estimating complex production functions: The importance of starting values
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.
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- 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.
- 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 149-70, February.
- C.J. O'Donnell & W.E. Griffiths, 2004.
"Estimating State-Contingent Production Frontiers,"
Department of Economics - Working Papers Series
911, The University of Melbourne.
- repec:tcd:wpaper:tep4 is not listed on IDEAS
- 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.
- 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).
- 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.
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