IDEAS home Printed from https://ideas.repec.org/p/boc/usug20/06.html
   My bibliography  Save this paper

Correlated random effects methods for panel data models with heterogeneous time effects

Author

Listed:
  • Jeff Wooldridge

    (Michigan State University, East Lansing, MI)

Abstract

I propose a correlated random effects (CRE) approach to linear panel data models with heterogeneous time effects. The setting is microeconometric, where the number of time periods is small relative to the number of cross-sectional units. Given T time periods, T different sources of heterogeneity are allowed, and each is allowed to be correlated with time-constant features of the covariates. In the leading case, the CRE approach extends the Mundlak regression by allowing each heterogeneity term to be correlated with the time averages of the time-varying covariates. Additional flexibility is allowed by extracting unit-specific trends from the covariates and using those in the CRE approach. Estimation requires (many) linear regressions. For small T, the approach is an alternative to factor models, which require nonlinear estimation in addition to pre-testing to determine the number of factors. I show straightforward implementation of the new estimators in Stata.

Suggested Citation

  • Jeff Wooldridge, 2020. "Correlated random effects methods for panel data models with heterogeneous time effects," London Stata Conference 2020 06, Stata Users Group.
    • Handle: RePEc:boc:usug20:06
    as

    Download full text from publisher

    File URL: http://repec.org/usug2020/Wooldridge_u20.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.

    More about this item

    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:boc:usug20:06. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.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.