IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v213y2019i1p54-67.html
   My bibliography  Save this article

Quantile-regression-based clustering for panel data

Author

Listed:
  • Zhang, Yingying
  • Wang, Huixia Judy
  • Zhu, Zhongyi

Abstract

In panel data analysis, it is important to identify subgroups of units with heterogeneous parameters. This can not only increase the model flexibility but also produce more efficient estimation by pooling information across units within the same group. In this paper, we propose a new quantile-regression-based clustering method for panel data. We develop an iterative algorithm using a similar idea of k-means clustering to identify subgroups with heterogeneous slopes at a single quantile level or across multiple quantiles. The asymptotic properties of the group membership estimator and corresponding group-specific slope estimator are established. The finite sample performance of the proposed method is assessed through simulation and the analysis of an economic growth data.

Suggested Citation

  • Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
    • Handle: RePEc:eee:econom:v:213:y:2019:i:1:p:54-67
    DOI: 10.1016/j.jeconom.2019.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407619300600
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2019.04.005?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. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    2. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    3. Shen, Xiaotong & Huang, Hsin-Cheng, 2010. "Grouping Pursuit Through a Regularization Solution Surface," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 727-739.
    4. Durlauf, Steven N & Johnson, Paul A, 1995. "Multiple Regimes and Cross-Country Growth Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 365-384, Oct.-Dec..
    5. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
    6. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    7. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    8. Durlauf, Steven N. & Kourtellos, Andros & Minkin, Artur, 2001. "The local Solow growth model," European Economic Review, Elsevier, vol. 45(4-6), pages 928-940, May.
    9. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    10. Lee, Kevin & Pesaran, M Hashem & Smith, Ron, 1997. "Growth and Convergence in Multi-country Empirical Stochastic Solow Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 357-392, July-Aug..
    11. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    12. Robert J. Barro, 1991. "Economic Growth in a Cross Section of Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(2), pages 407-443.
    13. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
    14. Fabio Canova, 2004. "Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 49-77, February.
    15. Antonio F. Galvao & Gabriel Montes-Rojas, 2015. "On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study," Econometrics, MDPI, vol. 3(3), pages 1-13, September.
    16. Tomohiro Ando & Jushan Bai, 2016. "Panel Data Models with Grouped Factor Structure Under Unknown Group Membership," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 163-191, January.
    17. Lin Chang-Ching & Ng Serena, 2012. "Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 1-14, August.
    18. Yunzhang Zhu & Xiaotong Shen & Wei Pan, 2013. "Simultaneous Grouping Pursuit and Feature Selection Over an Undirected Graph," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 713-725, June.
    19. Canova, Fabio & Marcet, Albert, 1995. "The Poor Stay Poor: Non-Convergence Across Countries and Regions," CEPR Discussion Papers 1265, C.E.P.R. Discussion Papers.
    20. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
    21. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    22. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    23. Tomohiro Ando & Jushan Bai, 2017. "Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1182-1198, July.
    24. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.
    25. Zhuoxin Sun & Ori Rosen & Allan R. Sampson, 2007. "Multivariate Bernoulli Mixture Models with Application to Postmortem Tissue Studies in Schizophrenia," Biometrics, The International Biometric Society, vol. 63(3), pages 901-909, September.
    26. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    27. Howard D. Bondell & Brian J. Reich, 2008. "Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR," Biometrics, The International Biometric Society, vol. 64(1), pages 115-123, March.
    28. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    29. Burnside, Craig, 1996. "Production function regressions, returns to scale, and externalities," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 177-201, April.
    30. Zheng Tracy Ke & Jianqing Fan & Yichao Wu, 2015. "Homogeneity Pursuit," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 175-194, March.
    31. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    32. Junhui Wang, 2010. "Consistent selection of the number of clusters via crossvalidation," Biometrika, Biometrika Trust, vol. 97(4), pages 893-904.
    33. Shujie Ma & Jian Huang, 2017. "A Concave Pairwise Fusion Approach to Subgroup Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 410-423, January.
    34. G. S. Maddala & S. Wu, 2000. "Cross-country growth regressions: problems of heterogeneity, stability and interpretation," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 635-642.
    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. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    2. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    3. Alexander Blasberg & Rüdiger Kiesel & Luca Taschini, 2022. "Carbon Default Swap - Disentangling the Exposure to Carbon Risk through CDS," CESifo Working Paper Series 10016, CESifo.
    4. Max Cytrynbaum, 2020. "Blocked Clusterwise Regression," Papers 2001.11130, arXiv.org.
    5. Jin, Peizhen & Wang, Siyu & Yin, Desheng & Zhang, Hang, 2023. "Environmental institutional supply that shapes a green economy: Evidence from Chinese cities," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    6. Blasberg, Alexander & Kiesel, Rüdiger & Taschini, Luca, 2023. "Carbon default swap – disentangling the exposure to carbon risk through CDS," LSE Research Online Documents on Economics 118096, London School of Economics and Political Science, LSE Library.
    7. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "K-expectiles clustering," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    8. Wu Wang & Ying Sun & Huixia Judy Wang, 2023. "Latent group detection in functional partially linear regression models," Biometrics, The International Biometric Society, vol. 79(1), pages 280-291, March.
    9. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    10. Giulia Livieri & Davide Radi & Elia Smaniotto, 2023. "Pricing Transition Risk with a Jump-Diffusion Credit Risk Model: Evidences from the CDS market," Papers 2303.12483, arXiv.org.
    11. Victor Muthama Musau & Carlo Gaetan & Paolo Girardi, 2022. "Clustering of bivariate satellite time series: A quantile approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    12. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    13. Chaudhary, Amit, 2021. "Do workers, managers, and stations matter for effective policing? A decomposition of productivity into three dimensions of unobserved heterogeneity," The Warwick Economics Research Paper Series (TWERPS) 1377, University of Warwick, Department of Economics.
    14. Tsubasa Ito & Shonosuke Sugasawa, 2023. "Grouped generalized estimating equations for longitudinal data analysis," Biometrics, The International Biometric Society, vol. 79(3), pages 1868-1879, September.
    15. Blasberg, Alexander & Kiesel, Rüdiger & Taschini, Luca, 2023. "Carbon default swap – disentangling the exposure to carbon risk through CDS," LSE Research Online Documents on Economics 118092, London School of Economics and Political Science, LSE Library.

    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. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    2. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    3. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2015. "Revisiting growth empirics based on IV panel quantile regression," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3859-3873, August.
    4. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    5. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Working Papers IES 2017/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2017.
    6. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    7. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    8. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    9. Wang, Wuyi & Su, Liangjun, 2021. "Identifying latent group structures in nonlinear panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 272-295.
    10. Philip Kostov & Julie Le Gallo, 2015. "Convergence: A Story of Quantiles and Spillovers," Kyklos, Wiley Blackwell, vol. 68(4), pages 552-576, November.
    11. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    12. Philip Kostov & Julie Le Gallo, 2018. "What role for human capital in the growth process: new evidence from endogenous latent factor panel quantile regressions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(5), pages 501-527, November.
    13. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    14. Xiao, Zhijie & Xu, Lan, 2019. "What do mean impacts miss? Distributional effects of corporate diversification," Journal of Econometrics, Elsevier, vol. 213(1), pages 92-120.
    15. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    16. Dogan, Eyup & Altinoz, Buket & Tzeremes, Panayiotis, 2020. "The analysis of ‘Financial Resource Curse’ hypothesis for developed countries: Evidence from asymmetric effects with quantile regression," Resources Policy, Elsevier, vol. 68(C).
    17. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    18. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    19. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    20. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.

    More about this item

    Keywords

    Fixed effects; Heterogeneity; Panel data; Quantile regression; Subgroup identification;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:eee:econom:v:213:y:2019:i:1:p:54-67. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.