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Inferences from Cross-Sectional, Stochastic Frontier Models

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Author Info

  • Leopold Simar
  • Paul Wilson

Abstract

Conventional approaches for inference about efficiency in parametric stochastic frontier (PSF) models are based on percentiles of the estimated distribution of the one-sided error term, conditional on the composite error. When used as prediction intervals, coverage is poor when the signal-to-noise ratio is low, but improves slowly as sample size increases. We show that prediction intervals estimated by bagging yield much better coverages than the conventional approach, even with low signal-to-noise ratios. We also present a bootstrap method that gives confidence interval estimates for (conditional) expectations of efficiency, and which have good coverage properties that improve with sample size. In addition, researchers who estimate PSF models typically reject models, samples, or both when residuals have skewness in the “wrong” direction, i.e., in a direction that would seem to indicate absence of inefficiency. We show that correctly specified models can generate samples with “wrongly” skewed residuals, even when the variance of the inefficiency process is nonzero. Both our bagging and bootstrap methods provide useful information about inefficiency and model parameters irrespective of whether residuals have skewness in the desired direction.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 29 (2010)
Issue (Month): 1 ()
Pages: 62-98

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Handle: RePEc:taf:emetrv:v:29:y:2010:i:1:p:62-98

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Related research

Keywords: Bagging; Bootstrap; Efficiency; Inference; Stochastic frontier;

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Cited by:
  1. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
  2. Arnab Bhattacharjee & Eduardo Castro & Chris Jensen-Butler, 2009. "Regional variation in productivity: a study of the Danish economy," Journal of Productivity Analysis, Springer, vol. 31(3), pages 195-212, June.
  3. Raimundo Soto & Rosalía Vásquez, 2011. "The Efficiency Cost of the Kafala in Dubai: A Stochastic Frontier Analysis," Documentos de Trabajo 399, Instituto de Economia. Pontificia Universidad Católica de Chile..
  4. Léopold Simar, 2007. "How to improve the performances of DEA/FDH estimators in the presence of noise?," Journal of Productivity Analysis, Springer, vol. 28(3), pages 183-201, December.
  5. Arnab Bhattacharjee & Eduardo de Castro & (Late) Chris Jensen-Butler, 2007. "Evaluating Economic Theories of Growth and Inequality: A Study of the Danish Economy," CDMA Working Paper Series 200723, Centre for Dynamic Macroeconomic Analysis.
  6. Wei Wang & Christine Amsler & Peter Schmidt, 2011. "Goodness of fit tests in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 35(2), pages 95-118, April.
  7. Haelermans, Carla & De Witte, Kristof, 2012. "The role of innovations in secondary school performance – Evidence from a conditional efficiency model," European Journal of Operational Research, Elsevier, vol. 223(2), pages 541-549.
  8. Valentin Zelenyuk, 2011. "Scale Efficiency: Equivalence of Primal and Dual Measures," CEPA Working Papers Series WP092011, School of Economics, University of Queensland, Australia.
  9. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
  10. Leopold Simar & Ingrid Van Keilegom & Valentin Zelenyuk, 2014. "Nonparametric Least Squares Methods for Stochastic Frontier Models," CEPA Working Papers Series WP032014, School of Economics, University of Queensland, Australia.

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