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On the Accuracy of Bootstrap Confidence Intervals for Efficiency Levels in Stochastic Frontier Models with Panel Data

  • Myungsup Kim

    (University of North Texas)

  • Yangseon Kim

    (East-West Center)

  • Peter Schmidt

    (Michigan State University)

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the per- centile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid only for large time-series sample size (T), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets.

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Paper provided by University of Crete, Department of Economics in its series Working Papers with number 0704.

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Length: 39 pages
Date of creation: 00 Oct 2006
Date of revision:
Handle: RePEc:crt:wpaper:0704
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  1. Gary Koop & M. F. J. Steel, 2004. "Bayesian Analysis of Stochastic Frontier Models," ESE Discussion Papers 19, Edinburgh School of Economics, University of Edinburgh.
  2. SIMAR, Léopold, . "Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric metods with bootstrapping," CORE Discussion Papers RP -995, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Hall, P. & Härdle, W. & Simar, L., . "Iterated bootstrap with applications to frontier models," CORE Discussion Papers RP -1145, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. HALL, Peter & HÄRDLE, Wolfgang & SIMAR, Léopold, . "On the inconsistency of bootstrap distribution estimators," CORE Discussion Papers RP -1062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. 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).
  6. PARK, Byeong & SIMAR, Léopold, 1992. "Efficient semiparametric estimation in stochastic frontier model," CORE Discussion Papers 1992013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  8. 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.
  9. 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.
  10. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
  11. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1989. "Production Frontiers With Cross-Sectinal And Time-Series Variation In Efficiency Levels," Working Papers 89-18, C.V. Starr Center for Applied Economics, New York University.
  12. repec:dgr:kubcen:199603 is not listed on IDEAS
  13. 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.
  14. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
  15. 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.
  16. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
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