IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v52y2017i1d10.1007_s00181-016-1072-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-016-1072-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-016-1072-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
    4. 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.
    5. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    6. Fan, Jianqing & Yao, Qiwei & Tong, Howell, 1996. "Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems," LSE Research Online Documents on Economics 6704, London School of Economics and Political Science, LSE Library.
    7. Howard D. Bondell & Brian J. Reich & Huixia Wang, 2010. "Noncrossing quantile regression curve estimation," Biometrika, Biometrika Trust, vol. 97(4), pages 825-838.
    8. 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.
    9. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
    10. Shin-Yi Chou & Jin-Tan Liu & Cliff J. Huang, 2004. "Health insurance and savings over the life cycle-a semiparametric smooth coefficient estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 295-322.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
    2. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    3. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    4. Juan Carlos Matallín-Sáez & Amparo Soler-Domínguez & Emili Tortosa-Ausina, 2019. "Does active management add value? New evidence from a quantile regression approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1734-1751, October.
    5. J. Carlos Matallín-Sáez & Amparo Soler-Domínguez & Emili Tortosa-Ausina, 2013. "Does active management add value? New evidence from a quantile regression," Working Papers 2013/01, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. 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.
    7. repec:wyi:journl:002095 is not listed on IDEAS
    8. Sofia KOURTESI & Panos FOUSEKIS & Apostolos POLYMEROS, 2012. "Conditional Efficiency Estimation With Environmental Variables: Evidence From Greek Cereal Farms," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 11(1), pages 43-52.
    9. repec:wyi:journl:002112 is not listed on IDEAS
    10. Cai, Zongwu & Wang, Xian, 2008. "Nonparametric estimation of conditional VaR and expected shortfall," Journal of Econometrics, Elsevier, vol. 147(1), pages 120-130, November.
    11. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    12. Cai, Zongwu & Xiao, Zhijie, 2012. "Semiparametric quantile regression estimation in dynamic models with partially varying coefficients," Journal of Econometrics, Elsevier, vol. 167(2), pages 413-425.
    13. Zongwu Cai & Qi Li, 2013. "Some Recent Develop- ments on Nonparametric Econometrics," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    14. repec:wyi:journl:002114 is not listed on IDEAS
    15. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
    16. Behr, Andreas, 2010. "Quantile regression for robust bank efficiency score estimation," European Journal of Operational Research, Elsevier, vol. 200(2), pages 568-581, January.
    17. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    18. Atwood, Joseph & Shaik, Saleem, 2020. "Theory and statistical properties of Quantile Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 286(2), pages 649-661.
    19. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    20. Simar, Léopold & Vanhems, Anne, 2012. "Probabilistic characterization of directional distances and their robust versions," Journal of Econometrics, Elsevier, vol. 166(2), pages 342-354.
    21. Martins-Filho, Carlos & Ziegelmann, Flávio Augusto & Torrent, Hudson da Silva, 2013. "Local Exponential Frontier Estimation," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 33(2), November.
    22. Amparo Soler Domínguez & Juan Carlos Matallín Sáez & Emili Tortosa Ausina, 2011. "On the informativeness of persistence for mutual funds' performance evaluation using partial frontiers," Working Papers. Serie EC 2011-08, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    23. Nolwenn Roudaut & Anne Vanhems, 2012. "Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach," Journal of Productivity Analysis, Springer, vol. 37(2), pages 155-169, April.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:52:y:2017:i:1:d:10.1007_s00181-016-1072-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.