IDEAS home Printed from https://ideas.repec.org/p/zur/iewwpx/521.html
   My bibliography  Save this paper

Leverage and covariance matrix estimation in finite-sample IV regressions

Author

Listed:
  • Andreas Steinhauer
  • Tobias Wuergler

Abstract

This paper develops basic algebraic concepts for instrumental variables (IV) regressions which are used to derive the leverage and influence of observations on the 2SLS estimate and compute alternative heteroskedasticity-consistent (HC1, HC2 and HC3) estimators for the 2SLS covariance matrix in a finite-sample context. Monte Carlo simulations and applications to growth regressions are used to evaluate the performance of these estimators. The results support the use of HC3 instead of White�s robust standard errors in small and unbalanced data sets. The leverage and influence of observations can be examined with the various measures derived in the paper.

Suggested Citation

  • Andreas Steinhauer & Tobias Wuergler, 2010. "Leverage and covariance matrix estimation in finite-sample IV regressions," IEW - Working Papers 521, Institute for Empirical Research in Economics - University of Zurich.
    • Handle: RePEc:zur:iewwpx:521
    as

    Download full text from publisher

    File URL: https://www.econ.uzh.ch/apps/workingpapers/wp/iewwp521.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    2. Damian Kozbur, 2017. "Testing-Based Forward Model Selection," American Economic Review, American Economic Association, vol. 107(5), pages 266-269, May.
    3. Maurice J.G. Bun & Teresa D. Harrison, 2014. "OLS and IV estimation of regression models including endogenous interaction terms," UvA-Econometrics Working Papers 14-02, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Sin, C.Y. (Chor-yiu) & Lee, Cheng-Few, 2021. "Using heteroscedasticity-non-consistent or heteroscedasticity-consistent variances in linear regression," Econometrics and Statistics, Elsevier, vol. 18(C), pages 117-142.

    More about this item

    Keywords

    Two stage least squares; leverage; influence; heteroskedasticity-consistent covariance matrix estimation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single 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:zur:iewwpx:521. 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: Severin Oswald (email available below). General contact details of provider: .

    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.