IDEAS home Printed from https://ideas.repec.org/a/eee/ecosta/v34y2025icp14-31.html
   My bibliography  Save this article

A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data

Author

Listed:
  • Drukker, David M.
  • Liu, Di

Abstract

New methods are discussed for estimating the population-averaged (PA) coefficients of some variables of interest in a sparse, high-dimensional generalized linear model (GLM) in an unbalanced panel-data setting with random effects. A cluster plugin for GLM lassos with unbalanced panel data is proposed. It is proven that a lasso that uses the new cluster-plugin method can outperform a lasso using the cross-sectional plugin, when the covariates have non-zero within-panel covariances. The proposed cluster plugin for GLMs extends the literature on Neyman-orthogonal moment conditions and provides estimators for the PA coefficients in sparse, high-dimensional logit, Poisson, and linear models when the data come from unbalanced panels. The results of the Monte Carlo simulations show that the implemented estimators perform well in finite samples. The simulations also show that a GLM lasso using the proposed cluster-plugin method produces more accurate covariate selection than a GLM lasso using the cross-sectional plugin method, when the covariates have non-zero within-panel covariances. Easy-to-use Stata commands are available for the proposed methods.

Suggested Citation

  • Drukker, David M. & Liu, Di, 2025. "A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data," Econometrics and Statistics, Elsevier, vol. 34(C), pages 14-31.
    • Handle: RePEc:eee:ecosta:v:34:y:2025:i:c:p:14-31
    DOI: 10.1016/j.ecosta.2022.02.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2452306222000132
    Download Restriction: Full text for ScienceDirect subscribers only. Contains open access articles
    File URL: https://libkey.io/10.1016/j.ecosta.2022.02.006?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.

    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:ecosta:v:34:y:2025:i:c:p:14-31. 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.

    We have no bibliographic 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.

    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: https://www.journals.elsevier.com/econometrics-and-statistics .

    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.