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Random Coefficients in Unbalanced Panels: An Application on Data from Chemical Plants

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  • Erik Biorn
  • Kjerti-Gro Lindquist
  • Terje Skjerpen

Abstract

A framework for analyzing substitution and scale properties from plant-level panel data is presented. Focus is on comparing the constant and random coefficient specification of the substitution and scale parameters and investigating the variation of the parameters across plants. Characteristics of the model framework are (i) an equation system consisting of a three-factor translog cost function and the corresponding cost-share equations, (ii) random plant specific heterogeneity in coefficients, and (iii) a Maximum Likelihood procedure allowing for unbalanced panel data. The empirical results, based on data from Norwegian chemical plants, indicate pronounced plant specific heterogeneity in substitution and scale properties. Substantial parts of the variances of the cost and the cost shares can be ascribed to variation in the coefficient vector. The estimated mean scale properties are considerably influenced by the choice of model specification, while conclusions regarding price effects are more robust.

Suggested Citation

  • Erik Biorn & Kjerti-Gro Lindquist & Terje Skjerpen, 2003. "Random Coefficients in Unbalanced Panels: An Application on Data from Chemical Plants," Annals of Economics and Statistics, GENES, issue 69, pages 55-83.
  • Handle: RePEc:adr:anecst:y:2003:i:69:p:55-83
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    File URL: http://www.jstor.org/stable/20076363
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    Citations

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    Cited by:

    1. Kjersti-Gro Lindquist, 2002. "The Effect of New Technology in Payment Services on Banks' Intermediation," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B3-2, International Conferences on Panel Data.
    2. Biorn, Erik & Hagen, Terje P. & Iversen, Tor & Magnussen, Jon, 2006. "Heterogeneity in Hospitals' Responses to a Financial Reform: A Random Coefficient Analysis of The Impact of Activity-Based Financing on Efficiency," MPRA Paper 8169, University Library of Munich, Germany.
    3. Robert Chambers & Giannis Karagiannis & Vangelis Tzouvelekas, 2014. "Productivity accounting for separable technologies," Journal of Productivity Analysis, Springer, vol. 41(1), pages 41-50, February.
    4. Erik Biørn & Kjersti-Gro Lindquist & Terje Skjerpen, 2002. "Heterogeneity in Returns to Scale: A Random Coefficient Analysis with Unbalanced Panel Data," Journal of Productivity Analysis, Springer, vol. 18(1), pages 39-57, July.
    5. Daniel Atsbeha & Dadi Kristofersson & Kyrre Rickertsen, 2015. "Broad breeding goals and production costs in dairy farming," Journal of Productivity Analysis, Springer, vol. 43(3), pages 403-415, June.
    6. Erik Biørn & Terje Hagen & Tor Iversen & Jon Magnussen, 2010. "How different are hospitals’ responses to a financial reform? The impact on efficiency of activity-based financing," Health Care Management Science, Springer, vol. 13(1), pages 1-16, March.
    7. Biorn,E., 1999. "Random coefficients in regression equation systems : the case with unbalanced panel data," Memorandum 27/1999, Oslo University, Department of Economics.
    8. Erik Biørn & Terje Skjerpen, 2002. "Aggregation and Aggregation Biases in Production Functions: A Panel Data Analysis of Translog Models," Discussion Papers 317, Statistics Norway, Research Department.
    9. Biørn, Erik & Skjerpen, Terje & Wangen, Knut Reidar, 2004. "Can Random Coefficient Cobb-Douglas Production Functions Be Aggregated to Similar Macro Functions?," Memorandum 22/2004, Oslo University, Department of Economics.

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