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On ranking and selection from independent truncated normal distributions

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  • Horrace, William C.

Abstract

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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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 126 (2005)
Issue (Month): 2 (June)
Pages: 335-354

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Handle: RePEc:eee:econom:v:126:y:2005:i:2:p:335-354

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Web page: http://www.elsevier.com/locate/jeconom

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References

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  1. KOOP , Gary & OSIEWALSKI , Jacek & STEEL , Mark, 1995. "Bayesian Efficiency Analysis through Individual Effects : Hospital Cost Frontiers," CORE Discussion Papers 1995036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  3. Tsionas, E.G., 2001. "Stochastic Frontier Models with Random Coefficients," Athens University of Economics and Business 130, Athens University of Economics and Business, Department of International and European Economic Studies.
  4. Hong, Han & Shum, Matthew, 2003. "Econometric models of asymmetric ascending auctions," Journal of Econometrics, Elsevier, vol. 112(2), pages 327-358, February.
  5. Fernandez C. & Koop G. & Steel M.F.J., 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 432-442, June.
  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. 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.
  8. 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.
  9. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
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Citations

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Cited by:
  1. Ali Genç, 2013. "Moments of truncated normal/independent distributions," Statistical Papers, Springer, vol. 54(3), pages 741-764, August.
  2. Ronald Felthoven & William Horrace & Kurt Schnier, 2009. "Estimating heterogeneous capacity and capacity utilization in a multi-species fishery," Journal of Productivity Analysis, Springer, vol. 32(3), pages 173-189, December.
  3. Felthoven, Ronald G. & Horrace, William C. & Schnier, Kurt E., 2006. "Estimating Heterogeneous Primal Capacity and Capacity Utilization Measures in a Multi-Species Fishery," 2006 Annual meeting, July 23-26, Long Beach, CA 21276, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  4. Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2007. "Identifying technically efficient fishing vessels: a non-empty, minimal subset approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 729-745.
  5. William Horrace & Seth Richards-Shubik, 2012. "A Monte Carlo study of ranked efficiency estimates from frontier models," Journal of Productivity Analysis, Springer, vol. 38(2), pages 155-165, October.
  6. Phill Wheat & William Greene & Andrew Smith, 2014. "Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 55-65, August.
  7. William C. Horrace & Christopher F. Parmeter, 2014. "A Laplace Stochastic Frontier Model," Center for Policy Research Working Papers 166, Center for Policy Research, Maxwell School, Syracuse University.
  8. William Horrace & Seth Richards-Shubik, 2013. "Expected Efficiency Ranks From Parametric Stochastic Fronteir Models," Center for Policy Research Working Papers 153, Center for Policy Research, Maxwell School, Syracuse University.
  9. William C. Horrace & Seth O. Richards, 2007. "A Monte Carlo Study of Efficiency Estimates from Frontier Models," Center for Policy Research Working Papers 97, Center for Policy Research, Maxwell School, Syracuse University.
  10. Jason J. Sharples & John C. V. Pezzey, 2005. "Expectations of linear functions with respect to truncazted multinormal distributions, with applications for uncertainty analysis in environmental modelling," Economics and Environment Network Working Papers 0503, Australian National University, Economics and Environment Network.

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