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Omitted Variables and Misspecified Disturbances in the Logit Model

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  • J.S. Cramer

    (University of Amsterdam)

Abstract

In binary discrete regression models like logit or probit the omis-sion of a relevant regressor (even if it is orthogonal) depresses the re-maining b coefficients towards zero. For the probit model, Wooldridge(2002) has shown that this bias does not carry over to the effect ofthe regressor on the outcome. We find by simulations that this alsoholds for logit models, even when the omitted variable leads to severemisspecification of the disturbance. More simulations show that es-timates of these effects by logit analysis are also impervious to puremisspecification of the disturbance.

Suggested Citation

  • J.S. Cramer, 2005. "Omitted Variables and Misspecified Disturbances in the Logit Model," Tinbergen Institute Discussion Papers 05-084/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20050084
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    References listed on IDEAS

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    Cited by:

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    2. Massimiliano Bratti & Daniele Checchi & Guido De Blasio, 2008. "Does the Expansion of Higher Education Increase the Equality of Educational Opportunities? Evidence from Italy," LABOUR, CEIS, vol. 22(s1), pages 53-88, June.
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    6. Malakhov, Dmitry, 2018. "Internet usage and TV and online media trust: Case of Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 67-89.

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    More about this item

    Keywords

    logit model; omitted variables; misspecification;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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