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On the Use of Panel Data in Bayesian Stochastic Frontier Models

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  • Fernández, C.
  • Osiewalski, J.
  • Steel, M.F.J.

    (Tilburg University, Center for Economic Research)

Abstract

We consider a Bayesian analysis of the stochastic frontier model with composed error.Under a commonly used class of (partly) noninformative prior distributions, the existence of the posterior distribution and of posterior moments is examined.Viewing this model as a Normal linear regression model with regression parameters corresponding to both the frontier and the inefficiency terms, generates the insights used to derive results in a very wide framework.It is found that in pure cross-section models posterior inference is precluded under this ``usual'' class of priors.Existence of a well-defined posterior distribution crucially hinges upon the structure imposed on the inefficiency terms.Exploiting panel data naturally suggests the use of more structured models, where Bayesian inference can be conducted.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1996-17.

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Date of creation: 1996
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Handle: RePEc:dgr:kubcen:199617

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Related research

Keywords: panel data; bayesian statistics; stochastic frontier models;

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References

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  1. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
  2. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
  3. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
  4. Fernández, C. & Osiewalski, J. & Steel, M.F.J., 1995. "Inference robustness in multivariate models with a scale parameter," Discussion Paper 1995-25, Tilburg University, Center for Economic Research.
  5. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
  6. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
  7. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1995. "The components of output growth: A cross-country analysis," Discussion Paper 1995-17, Tilburg University, Center for Economic Research.
  8. 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.
  9. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  10. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  11. KOOP, Gary & STEEL, Mark F. & OSIEWALSKI, Jacek, 1994. "Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling," CORE Discussion Papers 1994061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  13. Osiewalski, J. & Steel, M.F.J., 1996. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Discussion Paper 1996-03, Tilburg University, Center for Economic Research.
  14. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  15. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Bayesian efficiency analysis with a flexible form: The aim cost function," Discussion Paper 1994-13, Tilburg University, Center for Economic Research.
  16. John F. Geweke, 1995. "Posterior simulators in econometrics," Working Papers 555, Federal Reserve Bank of Minneapolis.
  17. 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.
  18. repec:fth:louvco:9530 is not listed on IDEAS
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Citations

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Cited by:
  1. Gary Koop, 1998. "Carbon dioxide emissions and economic growth: A structural approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 489-515.
  2. Supawat Rungsuriyawiboon & Chris O'Donnell, 2004. "Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities," CEPA Working Papers Series WP072004, School of Economics, University of Queensland, Australia.

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