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Stochastic error specification in primal and dual production systems

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  • Subal C. Kumbhakar
  • Efthymios G. Tsionas

Abstract

In this paper we derive both primal and dual-cost systems in which the stochastic specifications arise from the model (random environment or measurement errors and optimization errors)—not tacked on at the end after the deterministic system is worked out. Derivation of the error structures is based on cost‐minimizing behavior on the firms. The primal systems constitute the production function and the first‐order conditions of cost minimization. We consider two dual‐cost systems. The first dual system is based on the cost function and cost share equations. The second dual system is based on a multiplicative general error production model that is an alternative to McElroy's additive general error production model. Our multiplicative general error model gives a clear and intuitive economic meaning to the error components. The resulting cost system is easy to estimate compared to the alternative cost systems. The error components in the multiplicative general error model can capture heterogeneity in the technology parameters even in a cross‐sectional model. Panel data are not necessary to estimate either the primal or dual systems. The models are estimated using data on 72 fossil fuel‐fired steam electric power generation plants (observed for the period 1986–1999) in the USA. Copyright (C) 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Subal C. Kumbhakar & Efthymios G. Tsionas, 2011. "Stochastic error specification in primal and dual production systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 270-297, March.
  • Handle: RePEc:wly:japmet:v:26:y:2011:i:2:p:270-297
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    File URL: http://hdl.handle.net/10.1002/jae.1100
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    Cited by:

    1. Delis, Manthos D. & Hasan, Iftekhar & Tsionas, Efthymios G., 2015. "Firms’ risk endogenous to strategic management choices," Research Discussion Papers 16/2015, Bank of Finland.
    2. Tian, Xu & Sun, Feifei & Zhou, Yingheng, 2015. "Technical Efficiency and Its Determinants in China's Hog Production," 2015 Conference, August 9-14, 2015, Milan, Italy 212718, International Association of Agricultural Economists.
    3. Badunenko, Oleg & Kumbhakar, Subal C., 2016. "When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models," European Journal of Operational Research, Elsevier, vol. 255(1), pages 272-287.
    4. Oleg Badunenko & Daniel J. Henderson & Subal C. Kumbhakar, 2012. "When, where and how to perform efficiency estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 863-892, October.
    5. Delis, Manthos D. & Hasan, Iftekhar & Tsionas, Efthymios G., 2014. "The risk of financial intermediaries," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 1-12.
    6. repec:sbe:breart:v:31:y:2011:i:2:a:4065 is not listed on IDEAS
    7. Subal Kumbhakar & Kai Sun, 2012. "Estimation of TFP growth: a semiparametric smooth coefficient approach," Empirical Economics, Springer, vol. 43(1), pages 1-24, August.
    8. repec:bof:bofrdp:urn:nbn:fi:bof-201508211363 is not listed on IDEAS
    9. Xi Chen, 2011. "Increasing Returns to Scale in U.S. manufacturing industries: evidence from direct and reverse regression," Working Papers of BETA 2011-11, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    10. Delis, Manthos & Hasan, Iftekhar & Tsionas, Efthymios, 2015. "Banks’ Risk Endogenous to Strategic Management Choices," MPRA Paper 64907, University Library of Munich, Germany.
    11. Kumbhakar, Subal C., 2012. "Specification and estimation of primal production models," European Journal of Operational Research, Elsevier, vol. 217(3), pages 509-518.
    12. Kumbhakar, Subal C. & Lai, Hung-pin, 2016. "Maximum likelihood estimation of the revenue function system with output-specific technical efficiency," Economics Letters, Elsevier, vol. 138(C), pages 42-45.

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