IDEAS home Printed from https://ideas.repec.org/p/zbw/zewdip/22023.html
   My bibliography  Save this paper

Estimation of group structures in panel models with individual fixed effects

Author

Listed:
  • Mammen, Enno
  • Wilke, Ralf A.
  • Zapp, Kristina Maria

Abstract

The fixed effects (FE) panel model is one of the main econometric tools in empirical economic research. A major practical limitation is that the parameters on time-constant covariates are not identifiable. This paper presents a new approach to grouping FE in the linear panel model to reduce their dimensionality and ensure identifiability. By using unsupervised nonparametric density based clustering, cluster patterns including their location and number are not restricted. The approach works with large data structures (units and groups) and only clusters units that are sufficiently similar, while leaving others as unclustered atoms. Asymptotic theory and rates of convergence are presented. With the help of simulations and an application to economic data it is shown that the suggested method performs well and gives more insightful and efficient results than conventional panel models.

Suggested Citation

  • Mammen, Enno & Wilke, Ralf A. & Zapp, Kristina Maria, 2022. "Estimation of group structures in panel models with individual fixed effects," ZEW Discussion Papers 22-023, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:22023
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/261375/1/1810233224.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gerhard Tutz & Gunther Schauberger, 2015. "Extended ordered paired comparison models with application to football data from German Bundesliga," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 209-227, April.
    2. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    3. Stéphane Bonhomme & Thibaut Lamadon & Elena Manresa, 2022. "Discretizing Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 90(2), pages 625-643, March.
    4. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
    5. Gerhard Tutz & Margret-Ruth Oelker, 2017. "Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures," International Statistical Review, International Statistical Institute, vol. 85(2), pages 204-227, August.
    6. Ganzer, Andreas & Schmucker, Alexandra & Vom Berge, Philipp & Wurdack, Anja, 2017. "Sample of integrated labour market biographies - regional file 1975-2014 : (SIAB-R 7514)," FDZ Datenreport. Documentation on Labour Market Data 201701_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. repec:iab:iabfda:201701(en is not listed on IDEAS
    8. Hahn, Jinyong & Moon, Hyungsik Roger, 2010. "Panel Data Models With Finite Number Of Multiple Equilibria," Econometric Theory, Cambridge University Press, vol. 26(3), pages 863-881, June.
    9. Chatterjee, A. & Lahiri, S. N., 2011. "Bootstrapping Lasso Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 608-625.
    10. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    11. Wickham, Hadley, 2011. "The Split-Apply-Combine Strategy for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i01).
    12. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    13. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    14. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    15. Rohart, Florian & San Cristobal, Magali & Laurent, Béatrice, 2014. "Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 209-222.
    16. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    17. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
    18. Heiler, Phillip & Mareckova, Jana, 2021. "Shrinkage for categorical regressors," Journal of Econometrics, Elsevier, vol. 223(1), pages 161-189.
    19. Jürg Schelldorfer & Peter Bühlmann & Sara Van De Geer, 2011. "Estimation for High‐Dimensional Linear Mixed‐Effects Models Using ℓ 1 ‐Penalization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(2), pages 197-214, June.
    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. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.
    2. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    3. Shinsuke Asakawa, 2020. "Can Child Benefits Shape Parents' Attitudes toward Childrearing in Japan?: Effects of Child Benefit Policy Expansions," Discussion Papers in Economics and Business 19-04-Rev.2, Osaka University, Graduate School of Economics.
    4. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    5. Davide Fiaschi & Elisa Giuliani, 2011. "The impact of business on society: exploring CRS adoption and alleged human rights abuses by large corporations," LEM Papers Series 2011/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Miomir Jovanović & Ljiljana Kašćelan & Aleksandra Despotović & Vladimir Kašćelan, 2015. "The Impact of Agro-Economic Factors on GHG Emissions: Evidence from European Developing and Advanced Economies," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    7. 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.
    8. Okui, Ryo & Wang, Wendun, 2021. "Heterogeneous structural breaks in panel data models," Journal of Econometrics, Elsevier, vol. 220(2), pages 447-473.
    9. Denis Chetverikov & Elena Manresa, 2022. "Spectral and post-spectral estimators for grouped panel data models," Papers 2212.13324, arXiv.org, revised Dec 2022.
    10. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Mar 2024.
    11. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    12. Dzemski, Andreas & Okui, Ryo, 2021. "Convergence rate of estimators of clustered panel models with misclassification," Economics Letters, Elsevier, vol. 203(C).
    13. Anatolitis, Vasilios & Azanbayev, Alina & Fleck, Ann-Katrin, 2022. "How to design efficient renewable energy auctions? Empirical insights from Europe," Energy Policy, Elsevier, vol. 166(C).
    14. Idriss Fontaine & Sabine Garabedian & David Nortes-Martinez & Helene Veremes, 2021. "Tropical Cyclones And Fertility : New Evidence From Madagascar," TEPP Working Paper 2021-02, TEPP.
    15. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    16. Anna Gloria Billé & Marco Rogna, 2022. "The effect of weather conditions on fertilizer applications: A spatial dynamic panel data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 3-36, January.
    17. Liu, Ruiqi & Shang, Zuofeng & Zhang, Yonghui & Zhou, Qiankun, 2020. "Identification and estimation in panel models with overspecified number of groups," Journal of Econometrics, Elsevier, vol. 215(2), pages 574-590.
    18. Sofia Costa & Marta Faias & Pedro Júdice & Pedro Mota, 2021. "Panel data modeling of bank deposits," Annals of Finance, Springer, vol. 17(2), pages 247-264, June.
    19. Latzman, Natasha E. & Lokey, Colby & Lesesne, Catherine A. & Klevens, Joanne & Cheung, Karen & Condron, Susanne & Garraza, Lucas Godoy, 2019. "An evaluation of welfare and child welfare system integration on rates of child maltreatment in Colorado," Children and Youth Services Review, Elsevier, vol. 96(C), pages 386-395.
    20. Niccolò Pisani & Javier Garcia‐Bernardo & Eelke Heemskerk, 2020. "Does it pay to be a multinational? A large‐sample, cross‐national replication assessing the multinationality–performance relationship," Strategic Management Journal, Wiley Blackwell, vol. 41(1), pages 152-172, January.

    More about this item

    Keywords

    Panel Data; Statistical Learning; Regularisation; Endogeneity;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:zewdip:22023. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zemande.html .

    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.