[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/lea/leawpi/0308.html
   My bibliography  Save this paper

Panel Binary Variables and Sufficiency: Generalizing Conditional Logit

Author

Listed:
  • Thierry Magnac
Abstract
This paper extends the conditional logit approach used in panel data models of binary variables with correlated fixed effects and strictly exogenous regressors. In a two-period two-state model, necessary and sufficient conditions on the joint distribution function of the individual-and-period specific shocks are given such that the sum of individual binary variables across time is a sufficient statistic for the individual effect. Under these conditions, root-n consistent conditional likelihood estimators exist. Moreover, it is shown by extending Chamberlain (1992) that root-n consistent regular estimators can be constructed in panel binary models if and only if the property of sufficiency holds. Imposing sufficiency is shown to reduce the dimensionality of the bivariate distribution function of the individual-and-period specific shocks. This setting is much less restrictive than the conditional logit approach (Rasch, Andersen, Chamberlain). In applied work, it amounts to quasi-difference the binary variables as if they were continuous variables and to transform a panel data model into a cross-section model. Semiparametric approaches can then be readily applied.

Suggested Citation

  • Thierry Magnac, 2003. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Research Unit Working Papers 0308, Laboratoire d'Economie Appliquee, INRA.
    • Handle: RePEc:lea:leawpi:0308
    as

    Download full text from publisher

    File URL: http://www.inra.fr/Internet/Departements/ESR/UR/lea/documents/wp/wp0308.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    Binary models; panel data; conditional logit; sufficiency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    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:lea:leawpi:0308. 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: Madeleine Roux (email available below). General contact details of provider: http://www.inra.fr/Internet/Departements/ESR/UR/lea/index.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.