Stochastic error specification in primal and dual production systems
AbstractIn 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 26 (2011)
Issue (Month): 2 (March)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
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- Badunenko, Oleg & Henderson, Daniel J. & Kumbhakar, Subal C., 2011.
"When, Where and How to Perform Efficiency Estimation,"
IZA Discussion Papers
5997, Institute for the Study of Labor (IZA).
- 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, Royal Statistical Society, vol. 175(4), pages 863-892, October.
- Oleg Badunenko & Daniel J. Henderson & Subal C. Kumbhakar, 2011. "When, where and how to perform efficiency estimation," Cologne Graduate School Working Paper Series, Cologne Graduate School in Management, Economics and Social Sciences 02-06, Cologne Graduate School in Management, Economics and Social Sciences.
- Badunenko, Oleg & Henderson, Daniel J. & Kumbhakar, Subal C., 2011. "When, where and how to perform efficiency estimation," MPRA Paper 33467, University Library of Munich, Germany.
- Kumbhakar, Subal C., 2012. "Specification and estimation of primal production models," European Journal of Operational Research, Elsevier, Elsevier, vol. 217(3), pages 509-518.
- Subal Kumbhakar & Kai Sun, 2012. "Estimation of TFP growth: a semiparametric smooth coefficient approach," Empirical Economics, Springer, Springer, vol. 43(1), pages 1-24, August.
- 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.
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