IDEAS home Printed from
   My bibliography  Save this paper

Generalized method of moments fitting of structural mean models


  • Tom Palmer

    (MRC CAiTE Centre, School of Social and Community Medicine, University of Bristol)

  • Roger Harbord
  • Paul Clarke
  • Frank Windmeijer


In this talk we describe how to fit structural mean models (SMMs), as proposed by Robins, using instrumental variables in the generalized method of moments (GMM) framework using Stata's gmm command. The GMM approach is flexible because it can fit overidentified models in which there are more instruments than endogenous variables. It also allows assessment of the joint validity of the instruments using Hansen's J test through Stata's estat overid gmm postestimation command. In the case of the logistic SMM, the approach also allows different first-stage association models. We show the relationship between the multiplicative SMM and the multiplicative GMM estimator implemented in the ivpois command of Nichols (2007). For the multiplicative SMM, we show—analogously to Imbens and Angrist (1994) for the linear case--that the estimate is a weighted average of local estimates using the instruments separately. To demonstrate the models, we use a Mendelian randomization example, in which genotypes found to be robustly associated with risk factors from genome-wide association studies are used as instrumental variables, thereby investigating the effect of being overweight on the risk of hypertension in the Copenhagen General Population Study.

Suggested Citation

  • Tom Palmer & Roger Harbord & Paul Clarke & Frank Windmeijer, 2011. "Generalized method of moments fitting of structural mean models," United Kingdom Stata Users' Group Meetings 2011 06, Stata Users Group.
  • Handle: RePEc:boc:usug11:06

    Download full text from publisher

    File URL:
    File Function: presentation slides
    Download Restriction: no

    File URL:
    File Function: handouts
    Download Restriction: no

    More about this item


    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:boc:usug11:06. 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: (Christopher F Baum). 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.