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Do we estimate an input or an output distance function? An application of the mixture approach to European railways

  • Subal Kumbhakar

    ()

  • Luis Orea
  • Ana Rodríguez-Álvarez
  • Efthymios Tsionas

In this paper, we estimate parametric input and output distance functions and discuss how to estimate a mixture/latent class model (LCM) involving the output and input distance functions in the context of multi-input and multi-output production technology. The proposed technique is applied to a panel data on European Railways (1971–1994). This model allows us to identify determinants of the efficiency orientation, thereby providing useful information that can help researchers to choose between the input and the output-oriented approaches. In addition, we develop cross-indices that can be used to compute input (output) technical inefficiency from the estimates of output (input) distance function. Copyright Springer Science+Business Media, LLC 2007

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File URL: http://hdl.handle.net/10.1007/s11123-006-0031-5
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Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 27 (2007)
Issue (Month): 2 (April)
Pages: 87-100

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Handle: RePEc:kap:jproda:v:27:y:2007:i:2:p:87-100
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=100296

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  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  2. Friebel, Guido & Ivaldi, Marc & Vibes, Catherine, 2004. "Railway (De)Regulation: A European Efficiency Comparison," CEPR Discussion Papers 4319, C.E.P.R. Discussion Papers.
  3. John Loizides & Efthymios G. Tsionas, 2004. "Dynamic Distributions of Productivity Growth in European Railways," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 38(1), pages 45-75, January.
  4. William Greene, 2001. "Fixed and Random Effects in Nonlinear Models," Working Papers 01-01, New York University, Leonard N. Stern School of Business, Department of Economics.
  5. Pedro Cantos & José Pastor & Lorenzo Serrano, 1999. "Productivity, efficiency and technical change in the European railways: A non-parametric approach," Transportation, Springer, vol. 26(4), pages 337-357, November.
  6. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
  7. Loizides, John & Tsionas, Efthymios G., 2002. "Productivity growth in European railways: a new approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(7), pages 633-644, August.
  8. Luis Orea & David Roibás & Alan Wall, 2004. "Choosing the Technical Efficiency Orientation to Analyze Firms' Technology: A Model Selection Test Approach," Journal of Productivity Analysis, Springer, vol. 22(1), pages 51-71, July.
  9. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
  10. Cantos, Pedro & Maudos, Joaqui­n, 2001. "Regulation and efficiency: the case of European railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(5), pages 459-472, June.
  11. José Manuel Pastor Monsálvez & Lorenzo Serrano Martínez & Pedro Cantos, 2002. "Cost And Revenue Inefficiencies In The European Railways," Working Papers. Serie EC 2002-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  12. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
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