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A Heteroscedastic Generalized Extreme Value Discrete Choice Model

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  • LANGCHE ZENG

    (The George Washington University)

Abstract
Of the commonly used discrete choice models, the probit class allows flexible covariance structures for disturbances but is computationally burdensome for problems with more than a few alternatives. The generalized extreme value (GEV) class, including the widely used logit and nested logit models, has the advantage of computational ease but suffers in general from the restriction of homoscedastic disturbances. This article generalizes the GEV class to allow heteroscedastic disturbances across decision makers as well as across choice alternatives. The resulting models include the heteroscedastic extreme value model as a special case, which is a generalized logit model with heteroscedasticity across choice alternatives. Particular attention is paid to the heteroscedastic logit and nested logit models because of their widespread use in practice. An empirical application reanalyzing data from the 1980 presidential election tests the hypothesis of information-induced heteroscedasticity across voters and finds support for a heteroscedastic logit model that reveals stronger effects of voter information on the turnout decision than suggested by the original standard logit model in Ordeshook and Zeng.

Suggested Citation

  • Langche Zeng, 2000. "A Heteroscedastic Generalized Extreme Value Discrete Choice Model," Sociological Methods & Research, , vol. 29(1), pages 118-144, August.
    • Handle: RePEc:sae:somere:v:29:y:2000:i:1:p:118-144
    DOI: 10.1177/0049124100029001006
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    References listed on IDEAS

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    Cited by:

    1. Paap, Richard & van Nierop, Erjen & van Heerde, Harald J. & Wedel, Michel & Franses, Philip Hans & Alsem, Karel Jan, 2005. "Consideration sets, intentions and the inclusion of "don't know" in a two-stage model for voter choice," International Journal of Forecasting, Elsevier, vol. 21(1), pages 53-71.
    2. Michel Bierlaire, 2006. "A theoretical analysis of the cross-nested logit model," Annals of Operations Research, Springer, vol. 144(1), pages 287-300, April.

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