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Semiparametric smooth coefficient quantile estimation of the production profile

Author

Listed:
  • Cliff J. Huang

    (Vanderbilt University)

  • Tsu-Tan Fu

    (Soochow University)

  • Hung-Pin Lai

    (National Chung Cheng University)

  • Yung-Lieh Yang

    (Ling Tung University)

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.

Suggested Citation

  • Cliff J. Huang & Tsu-Tan Fu & Hung-Pin Lai & Yung-Lieh Yang, 2017. "Semiparametric smooth coefficient quantile estimation of the production profile," Empirical Economics, Springer, vol. 52(1), pages 373-392, February.
    • Handle: RePEc:spr:empeco:v:52:y:2017:i:1:d:10.1007_s00181-016-1072-x
    DOI: 10.1007/s00181-016-1072-x
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    References listed on IDEAS

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    1. Howard D. Bondell & Brian J. Reich & Huixia Wang, 2010. "Noncrossing quantile regression curve estimation," Biometrika, Biometrika Trust, vol. 97(4), pages 825-838.
    2. Cai, Zongwu & Xu, Xiaoping, 2009. "Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 371-383.
    3. Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(2), pages 358-389, April.
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    6. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
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    9. Liu, Ting-Kun & Chen, Jong-Rong & Huang, Cliff J. & Yang, Chih-Hai, 2014. "Revisiting the productivity paradox: A semiparametric smooth coefficient approach based on evidence from Taiwan," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 300-308.
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    Citations

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    Cited by:

    1. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    2. Stefan Schweikl & Robert Obermaier, 2020. "Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects," Management Review Quarterly, Springer, vol. 70(4), pages 461-507, November.
    3. Hung-pin Lai & Cliff J. Huang & Tsu-Tan Fu, 2020. "Estimation of the production profile and metafrontier technology gap: a quantile approach," Empirical Economics, Springer, vol. 58(6), pages 2709-2731, June.
    4. Chung-Chu Chuang & Chung-Min Tsai & Hsiao-Chen Chang & Yi-Hsien Wang, 2021. "Applying Quantile Regression to Assess the Relationship between R&D, Technology Import and Patent Performance in Taiwan," JRFM, MDPI, vol. 14(8), pages 1-14, August.

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    More about this item

    Keywords

    Semiparametric smooth coefficient; Quantile estimation; Local linear; Stochastic frontier;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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