Semiparametric smooth coefficient quantile estimation of the production profile

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• 作者：Cliff J. Huang ; Tsu-Tan Fu ; Hung-Pin Lai ; Yung-Lieh Yang
• 关键词：Semiparametric smooth coefficient ; Quantile estimation ; Local linear ; Stochastic frontier
• 刊名：Empirical Economics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：52
• 期：1
• 页码：373-392
• 全文大小：
• 刊物主题：Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Economic Theory/Quantitative Economics/Mathematical Methods;
• 出版者：Springer Berlin Heidelberg
• ISSN：1435-8921
• 卷排序：52

文摘

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.