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Estimation and Inference in Parametric Stochastic Frontier Models: A SAS/IML Procedure for a Bootstrap Method

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  • Sylvie Tchumtchoua

    () (University of Connecticut)

Abstract

Parametric Stochastic Frontier Models are widely used in productivity analysis and are commonly estimated using FRONTIER, STATA or LIMDEP packages, which only provide point estimates for firm-specific technical efficiency. Confidence intervals for technical efficiencies with superior coverage properties than those offered by the Horrace and Schmidt (1996) method may be computed using the Bootstrap method introduced by Simar and Wilson (2005). To facilitate these calculations, we propose a SAS/IML procedure, which computes these confidence intervals for stochastic frontier models with or without inefficiency effects. We apply the program to estimating supermarket-specific technical efficiency in the U.S. Results indicates that the program works very well and produce narrower confidence intervals than those obtain using Horrace and Schmidt (1996) method.

Suggested Citation

  • Sylvie Tchumtchoua, 2006. "Estimation and Inference in Parametric Stochastic Frontier Models: A SAS/IML Procedure for a Bootstrap Method," Food Marketing Policy Center Research Reports 095, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
  • Handle: RePEc:zwi:fpcrep:095
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    File URL: http://fmpc.uconn.edu/publications/rr/rr95.pdf
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