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A Monte Carlo Study of Efficiency Estimates from Frontier Models

Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimated (a) the conditional means of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are more reliable in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance inthe stochastic frontier environment.

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Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 97.

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Length: 34 pages
Date of creation: Aug 2007
Date of revision:
Handle: RePEc:max:cprwps:97
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  1. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  2. Carmen Fernandez & Gary Koop & Mark F J Steel, 2004. "Multiple-output production with undesirable output: An application to nitrogen surplus in agriculture," ESE Discussion Papers 34, Edinburgh School of Economics, University of Edinburgh.
  3. 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.
  4. William C. Horrace, 2003. "On Ranking and Selection from Independent Truncated Normal Distributions," Econometrics 0306009, EconWPA.
  5. 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.
  6. 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).
  7. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  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. 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.
  10. 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.
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