IDEAS home Printed from https://ideas.repec.org/p/swe/wpaper/2017-18.html

Asymptotic Theory for Clustered Samples

Author

Listed:
  • Bruce E. Hansen

    (Department of Economics, University of Wisconsin-Madison)

  • Seojeong Jay Lee

    (School of Economics, UNSW Business School, UNSW Sydney)

Abstract

We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.

Suggested Citation

  • Bruce E. Hansen & Seojeong Jay Lee, 2017. "Asymptotic Theory for Clustered Samples," Discussion Papers 2017-18, School of Economics, The University of New South Wales.
    • Handle: RePEc:swe:wpaper:2017-18
    as

    Download full text from publisher

    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2017-18.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

    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:swe:wpaper:2017-18. 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: Hongyi Li (email available below). General contact details of provider: https://edirc.repec.org/data/senswau.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.