IDEAS home Printed from
   My bibliography  Save this paper

Mixed logit modeling in Stata--an overview


  • Arne Risa Hole

    (University of Sheffield)


The "workhorse" model for analysing discrete choice data, the conditional logit model, can be implemented in Stata using the official clogit and asclogit commands. While widely used, this model has several well-known limitations that have led researchers in various disciplines to consider more flexible alternatives. The mixed logit model extends the standard conditional logit model by allowing one or more of the parameters in the model to be randomly distributed. When one models the choices of individuals (as is common in several disciplines, including economics, marketing, and transport), this allows for preference of heterogeneity among respondents. Other advantages of the mixed logit model include the ability to allow for correlations across observations in cases where an individual made more than one choice, and relaxing the restrictive independence from the irrelevant alternatives property of the conditional logit model. There are a range of commands that can be used to estimate mixed logit models in Stata. With the exception of xtmelogit, the official Stata command for estimating binary mixed logit models, all of them are userwritten. The module that is probably best known is gllamm, but while very flexible, it can be slow when the model includes several random parameters. This talk will focus on alternative commands for estimating logit models, with focus on the mixlogit module. We will also look at alternatives and extensions to mixlogit, including the recent lclogit, bayesmlogit, and gmnl commands. The talk will review the theory behind the methods implemented by these commands and present examples of their use.

Suggested Citation

  • Arne Risa Hole, 2013. "Mixed logit modeling in Stata--an overview," United Kingdom Stata Users' Group Meetings 2013 23, Stata Users Group.
  • Handle: RePEc:boc:usug13:23

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LP, number fpsaus, December.
    Full references (including those not matched with items on IDEAS)

    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:boc:usug13:23. 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.