IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i2d10.1007_s00180-024-01526-w.html
   My bibliography  Save this article

Empirical likelihood change point detection in quantile regression models

Author

Listed:
  • Suthakaran Ratnasingam

    (California State University, San Bernardino)

  • Ramadha D. Piyadi Gamage

    (Western Washington University)

Abstract

Quantile regression is an extension of linear regression which estimates a conditional quantile of interest. In this paper, we propose an empirical likelihood-based non-parametric procedure to detect structural changes in the quantile regression models. Further, we have modified the proposed smoothed empirical likelihood-based method using adjusted smoothed empirical likelihood and transformed smoothed empirical likelihood techniques. We have shown that under the null hypothesis, the limiting distribution of the smoothed empirical likelihood ratio test statistic is identical to that of the classical parametric likelihood. Simulations are conducted to investigate the finite sample properties of the proposed methods. Finally, to demonstrate the effectiveness of the proposed method, it is applied to urinary Glycosaminoglycans (GAGs) data to detect structural changes.

Suggested Citation

  • Suthakaran Ratnasingam & Ramadha D. Piyadi Gamage, 2025. "Empirical likelihood change point detection in quantile regression models," Computational Statistics, Springer, vol. 40(2), pages 999-1020, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01526-w
    DOI: 10.1007/s00180-024-01526-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01526-w
    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/s00180-024-01526-w?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. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    2. Whang, Yoon-Jae, 2006. "Smoothed Empirical Likelihood Methods For Quantile Regression Models," Econometric Theory, Cambridge University Press, vol. 22(2), pages 173-205, April.
    3. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    4. Qibing Gao & Xiuqing Zhou & Yanqin Feng & Xiuli Du & XiaoXiao Liu, 2021. "An empirical likelihood method for quantile regression models with censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 75-96, January.
    5. Yichuan Zhao & Song Yang, 2008. "Empirical likelihood inference for censored median regression with weighted empirical hazard functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 441-457, June.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Cheng Yong Tang & Chenlei Leng, 2011. "Empirical likelihood and quantile regression in longitudinal data analysis," Biometrika, Biometrika Trust, vol. 98(4), pages 1001-1006.
    8. Gabriela Ciuperca & Zahraa Salloum, 2015. "Empirical likelihood test in a posteriori change-point nonlinear model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 919-952, November.
    9. Zhao, Yichuan & Chen, Feiming, 2008. "Empirical likelihood inference for censored median regression model via nonparametric kernel estimation," Journal of Multivariate Analysis, Elsevier, vol. 99(2), pages 215-231, February.
    Full references (including those not matched with items on IDEAS)

    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. Shuanghua Luo & Changlin Mei & Cheng-yi Zhang, 2017. "Smoothed empirical likelihood for quantile regression models with response data missing at random," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 95-116, January.
    2. Coad, Alex & Segarra, Agustí & Teruel, Mercedes, 2016. "Innovation and firm growth: Does firm age play a role?," Research Policy, Elsevier, vol. 45(2), pages 387-400.
    3. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    4. Maria Karadima & Helen Louri, 2019. "Non-performing loans in the euro area: does market power matter?," Working Papers 271, Bank of Greece.
    5. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    6. Sula, Ozan, 2008. "Demand for International Reserves: A Quantile Regression Approach," MPRA Paper 11680, University Library of Munich, Germany.
    7. Guilherme Resende Oliveira & Benjamin Miranda Tabak & José Guilherme de Lara Resende & Daniel Oliveira Cajueiro, 2012. "Determinantes da Estrutura de Capital das Empresas Brasileiras: uma abordagem em regressão quantílica," Working Papers Series 272, Central Bank of Brazil, Research Department.
    8. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    9. Antecol, Heather & Eren, Ozkan & Ozbeklik, Serkan, 2013. "The effect of Teach for America on the distribution of student achievement in primary school: Evidence from a randomized experiment," Economics of Education Review, Elsevier, vol. 37(C), pages 113-125.
    10. Gilles Dufrenot & Valerie Mignon & Charalambos Tsangarides, 2010. "The trade-growth nexus in the developing countries: a quantile regression approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(4), pages 731-761, December.
    11. Sula, Ozan, 2011. "Demand for international reserves in developing nations: A quantile regression approach," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 764-777, September.
    12. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    13. Joachim Wagner, 2016. "From Estimation Results to Stylized Facts: Twelve Recommendations for Empirical Research in International Activities of Heterogeneous Firms," World Scientific Book Chapters, in: Microeconometrics of International Trade, chapter 15, pages 479-514, World Scientific Publishing Co. Pte. Ltd..
    14. Xiaoming Lu & Zhaozhi Fan, 2015. "Weighted quantile regression for longitudinal data," Computational Statistics, Springer, vol. 30(2), pages 569-592, June.
    15. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    16. Kollias Christos & Paleologou Suzanna-Maria & Tzeremes Panayiotis, 2020. "Defence Spending and Unemployment in the USA: Disaggregated Analysis by Gender and Age Groups," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 26(2), pages 1-13, May.
    17. Stijn Kelchtermans & Reinhilde Veugelers, 2011. "The great divide in scientific productivity: why the average scientist does not exist," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 20(1), pages 295-336, February.
    18. Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    19. Karadima, Maria & Louri, Helen, 2020. "Non-performing loans in the euro area: Does bank market power matter?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    20. Chih-Chuan Yeh & Kuan-Min Wang & Yu-Bo Suen, 2011. "A quantile framework for analysing the links between inflation uncertainty and inflation dynamics across countries," Applied Economics, Taylor & Francis Journals, vol. 43(20), pages 2593-2602.

    More about this item

    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:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01526-w. 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.