IDEAS home Printed from
   My bibliography  Save this paper

Split-Sample Score Tests in Linear Instrumental Variables Regression


  • Saraswata Chaudhuri
  • Thomas Richardson
  • James Robins

    (Departments of Epidemiology and Biostatistics, Harvard University)

  • Eric Zivot


In this paper we design two split-sample tests for subsets of structural coefficients in a linear Instrumental Variables (IV) regression. Sample splitting serves two purposes – 1) validity of the resultant tests does not depend on the identifiability of the coefficients being tested and 2) it combines information from two unrelated samples one of which need not contain information on the dependent variable. The tests are performed on sub-sample one using the regression coefficients obtained from running the so-called first stage regression on subsample two (sample not containing information on the dependent variable). The first test uses the unbiased split-sample IV estimator of the remaining structural coefficients constrained by the hypothesized value of the structural coefficients of interest [see Angrist and Krueger (1995)]. We call this the USSIV score test. The USSIV score test is asymptotically equivalent to the standard score test based on sub-sample one when the standard regularity conditions are satisfied. However, the USSIV score test can be over-sized if the remaining structural coefficients are not identified. This motivates another test based on Robins (2004), which we call the Robins-test. The Robins-test is never oversized and if the remaining structural coefficients are identified, the Robins-test is asymptotically equivalent to USSIV score test against square-root-n local alternatives.

Suggested Citation

  • Saraswata Chaudhuri & Thomas Richardson & James Robins & Eric Zivot, 2007. "Split-Sample Score Tests in Linear Instrumental Variables Regression," Working Papers UWEC-2007-10, University of Washington, Department of Economics.
  • Handle: RePEc:udb:wpaper:uwec-2007-10

    Download full text from publisher

    File URL:
    Download Restriction: no


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

    Cited by:

    1. Chaudhuri, Saraswata & Zivot, Eric, 2011. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Journal of Econometrics, Elsevier, vol. 164(2), pages 239-251, October.
    2. Kapetanios, George & Khalaf, Lynda & Marcellino, Massimiliano, 2015. "Factor based identification-robust inference in IV regressions," CEPR Discussion Papers 10390, C.E.P.R. Discussion Papers.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:udb:wpaper:uwec-2007-10. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael Goldblatt). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.