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Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities Author info | Abstract | Publisher info | Download info | Related research | Statistics Supawat Rungsuriyawiboon
Chris O’Donnell () (CEPA - School of Economics, The University of Queensland )
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We estimate an input distance function for U.S. electric utilities under the assumption that non-negative variables associated with technical inefficiency are timeinvariant. We use Bayesian methodology to impose curvature restrictions implied by microeconomic theory and obtain exact finite-sample results for nonlinear functions of the parameters (eg. technical efficiency scores). We find that Bayesian point estimates of elasticities are more plausible than maximum likelihood estimates, technical efficiency scores from a random effects specification are higher than those obtained from a fixed effects model, and there is evidence of increasing returns to scale in the industry.
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Paper provided by School of Economics, University of Queensland, Australia in its series CEPA Working Papers Series with number
WP072004.
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Date of creation: Sep 2004Date of revision:
Handle: RePEc:qld:uqcepa:12Contact details of provider: Postal: St. Lucia, Qld. 4072 Phone: +61 7 3365 6570 Fax: +61 7 3365 7299 Email: Web page: http://www.uq.edu.au/economics/ More information through EDIRC
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