Semiparametric smooth coefficient quantile estimation of the production profile

Cliff J. Huang (), Tsu-Tan Fu (), Hung-pin Lai and Yung-Lieh Yang ()
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Cliff J. Huang: Vanderbilt University
Tsu-Tan Fu: Soochow University
Yung-Lieh Yang: Ling Tung University

Empirical Economics, 2017, vol. 52, issue 1, No 15, 373-392

Abstract: Abstract In this paper, quantile regression models are suggested as an alternative description of a production technology. The quantile of continuous order defines the production profile and the quantile-based individual technical efficiency relative to the quantile order. Quantile-based production frontier and efficiency are easy to derive and estimate and do not envelop all sample observation points. A quantile-based production frontier is more robust to extreme observations than DEA or FDH. Furthermore, quantile regression does not make a distribution assumption. It is more robust to the misspecification of error structure than DFA or SFA. In this paper, the quantile regression methods are extended to semiparametric smooth coefficient models. A local linear fitting scheme to estimate the smooth coefficients is proposed in the quantile framework. An empirical application of the model to the Taiwan manufacturing industry demonstrates the potential for the estimation of production technology and efficiency measures.

Keywords: Semiparametric smooth coefficient; Quantile estimation; Local linear; Stochastic frontier (search for similar items in EconPapers)
JEL-codes: C14 C21 C67 D24 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00181-016-1072-x

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