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Estimation of technical inefficiency effects using panel data and doubly heteroscedastic stochastic production frontiers

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  • K. Hadri
  • C. Guermat
  • J. Whittaker

Abstract

In previous studies, measures of technical inefficiency effects derived from stochastic production frontiers have been estimated from residuals which are sensitive to specification errors. This study corrects for this inaccuracy by extending the doubly heteroscedastic stochastic cost frontier suggested by Hadri (1999) to the model for technical inefficiency effects. This model is a stochastic frontier production function for panel data as proposed by Battese and Coelli (1995). The study uses, for illustration of the techniques, data on 101 mainly cereal farms in England. We find that the correction for heteroscedasticity is supported by the data. Both point estimates and confidence intervals for technical efficiencies are provided. The confidence intervals are constructed by extending the “Battese-Coelli” method reported by Horrace and Schmidt (1996) by allowing the technical inefficiency to be time varying and the disturbance terms to be heteroscedastic. The confidence intervals reveal the precision of technical efficiency estimates and show the deficiencies of making inferences based exclusively on point estimates. Copyright Springer-Verlag Berlin Heidelberg 2003

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 28 (2003)
Issue (Month): 1 (January)
Pages: 203-222

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Handle: RePEc:spr:empeco:v:28:y:2003:i:1:p:203-222

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Related research

Keywords: Key words: Stochastic frontier production; Heteroscedasticity; Technical efficiency; Elasticity; Panel data.; JEL classification: C23; C24; D24; Q12.;

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Cited by:
  1. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
  2. David I. Stern & Frank Jotzo, 2009. "How Ambitious are China and India's Emissions Intensity Targets?," Environmental Economics Research Hub Research Reports 1051, Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University.
  3. Ogundari, Kolawole & Brummer, Bernhard, 2010. "Estimating Technical Efficiency, Input substitution and complementary effects using Output Distance Function: A study of Cassava production in Nigeria," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 95773, African Association of Agricultural Economists (AAAE);Agricultural Economics Association of South Africa (AEASA).
  4. Tiziana Laureti, 2008. "Modelling Exogenous Variables in Human Capital Formation through a Heteroscedastic Stochastic Frontier," International Advances in Economic Research, Springer, vol. 14(1), pages 76-89, February.
  5. Andrew Tiffin, 2006. "Ukraine," IMF Working Papers 06/167, International Monetary Fund.
  6. Tchumtchoua, Sylvie, 2006. "Estimation and Inference in Parametric Stochastic Frontier Models: A SAS/IML Procedure for a Bootstrap Method," Research Reports 149177, University of Connecticut, Food Marketing Policy Center.
  7. Lowry, Mark Newton & Getachew, Lullit, 2009. "Statistical benchmarking in utility regulation: Role, standards and methods," Energy Policy, Elsevier, vol. 37(4), pages 1323-1330, April.
  8. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
  9. Arazmuradov, Annageldy & Martini, Gianmaria & Scotti, Davide, 2014. "Determinants of total factor productivity in former Soviet Union economies: A stochastic frontier approach," Economic Systems, Elsevier, vol. 38(1), pages 115-135.

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