IDEAS home Printed from https://ideas.repec.org/a/wly/emetrp/v83y2015i3p1147-1184.html
   My bibliography  Save this article

Grouped Patterns of Heterogeneity in Panel Data

Author

Listed:
  • Stéphane Bonhomme
  • Elena Manresa

Abstract

This paper introduces time‐varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a “grouped fixed‐effects” estimator that minimizes a least squares criterion with respect to all possible groupings of the cross‐sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.

Suggested Citation

  • Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    • Handle: RePEc:wly:emetrp:v:83:y:2015:i:3:p:1147-1184
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no

    Citations

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


    Cited by:

    1. Guner, Nezih & Kulikova, Yuliya & Llull, Joan, 2018. "Marriage and health: Selection, protection, and assortative mating," European Economic Review, Elsevier, vol. 104(C), pages 138-166.
    2. repec:wly:hlthec:v:26:y:2017:i:9:p:1146-1161 is not listed on IDEAS
    3. Lisa Oberlander & Anne‐Célia Disdier & Fabrice Etilé, 2017. "Globalisation and national trends in nutrition and health: A grouped fixed‐effects approach to intercountry heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(9), pages 1146-1161, September.
    4. Hansen, Christian & Liao, Yuan, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," MPRA Paper 75313, University Library of Munich, Germany.
    5. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    6. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised Mar 2018.
    7. repec:taf:gnstxx:v:29:y:2017:i:1:p:108-136 is not listed on IDEAS
    8. Ryo Okui & Takahide Yanagi, 2014. "Panel Data Analysis with Heterogeneous Dynamics," KIER Working Papers 906, Kyoto University, Institute of Economic Research.
    9. Falk Bräuning & Siem Jan Koopman, 2016. "The Dynamic Factor Network Model with an Application to Global Credit-Risk," Tinbergen Institute Discussion Papers 16-105/III, Tinbergen Institute.
    10. repec:eee:ecolec:v:142:y:2017:i:c:p:249-256 is not listed on IDEAS
    11. Francesco Bravo & Ba M. Chu & David T. Jacho-Chávez, 2017. "Semiparametric estimation of moment condition models with weakly dependent data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 108-136, January.
    12. Carlos Vladimir Rodríguez-Caballero, 2016. "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure," CREATES Research Papers 2016-31, Department of Economics and Business Economics, Aarhus University.
    13. Jiti Gao & Kai Xia, 2017. "Heterogeneous panel data models with cross-sectional dependence," Monash Econometrics and Business Statistics Working Papers 16/17, Monash University, Department of Econometrics and Business Statistics.
    14. Ruiqi Liu & Anton Schick & Zuofeng Shang & Yonghui Zhang & Qiankun Zhou, 2018. "Identification and estimation in panel models with overspecified number of groups," Departmental Working Papers 2018-03, Department of Economics, Louisiana State University.
    15. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Departmental Working Papers 201610, Rutgers University, Department of Economics.
    16. Chu, Ba, 2017. "Composite Quasi-Maximum Likelihood Estimation of Dynamic Panels with Group-Specific Heterogeneity and Spatially Dependent Errors," MPRA Paper 79709, University Library of Munich, Germany.
    17. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

    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:wly:emetrp:v:83:y:2015:i:3:p:1147-1184. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/essssea.html .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.