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On Ranking and Selection from Independent Truncated Normal Distributions

  • William C. Horrace

    (Syracuse University)

This paper develops probability statements and ranking and selection rules for independent truncated normal populations. An application to a broad class of parametric stochastic frontier models is considered, where interest centers on making probability statements concerning unobserved firm-level technical ineffciency. In particular, probabilistic decision rules allow subsets of firms to be deemed relatively effcient or ineffcient at pre-specified probabilities. An empirical example is provided.

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File URL: http://econwpa.repec.org/eps/em/papers/0306/0306009.pdf
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Paper provided by EconWPA in its series Econometrics with number 0306009.

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Length: 31 pages
Date of creation: 27 Jun 2003
Date of revision:
Handle: RePEc:wpa:wuwpem:0306009
Note: Type of Document - Acrobat PDF; prepared on IBM PC ; to print on HP; pages: 31 ; figures: included
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Carmen Fernandez & Gary Koop & Mark F.J. Steel, 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Econometrics 0201001, EconWPA, revised 06 Jan 2002.
  2. Tsionas, E.G., 2001. "Stochastic Frontier Models with Random Coefficients," DEOS Working Papers 130, Athens University of Economics and Business.
  3. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
  4. Koop, G. & Osiewalski, J. & Steel, M. F. J., . "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," CORE Discussion Papers RP -1245, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Amemiya, Takeshi, 1974. "Multivariate Regression and Simultaneous Equation Models when the Dependent Variables Are Truncated Normal," Econometrica, Econometric Society, vol. 42(6), pages 999-1012, November.
  6. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
  7. Han Hong & Matthew Shum, 2001. "Econometric Models of Asymmetric Ascending Auctions," Economics Working Paper Archive 453, The Johns Hopkins University,Department of Economics.
  8. 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.
  9. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  10. Horrace, William C., 2005. "Some results on the multivariate truncated normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 94(1), pages 209-221, May.
  11. William C. Horrace & Peter Schmidt, 2000. "Multiple comparisons with the best, with economic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-26.
  12. James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
  13. 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.
  14. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
  15. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
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