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Semiparametric Estimation of Stochastic Production Frontier Models

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  • Fan, Yanqin
  • Li, Qi
  • Weersink, Alfons

Abstract

This paper extends the linear stochastic frontier model proposed by D. J. Aigner, C. A. K. Lovell, and P. Schmidt (1977) to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudo-likelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte Carlo results show that the proposed estimators perform well in finite samples. An empirical application is presented. Extensions to a partially linear function and to more flexible one-sided error distributions than the half-normal are discussed.

Suggested Citation

  • Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
  • Handle: RePEc:bes:jnlbes:v:14:y:1996:i:4:p:460-68
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