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Using logit on big data. From iterative methods to analytical solutions

Author

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  • Birgit Stoltenberg

    (Nürnberg Institut für Marktentscheidungen)

Abstract

Numerical optimization for logit models is often time consuming, at least for big data. In this paper I combine several analytical approaches to solve logit models in a practical context including (sign) restrictions and constraints. The application to a household panel model compares the two approaches on several dimensions.

Suggested Citation

  • Birgit Stoltenberg, 2016. "Using logit on big data. From iterative methods to analytical solutions," NIM Working Paper wpaper-002, Nürnberg Institut für Marktentscheidungen e.V..
    • Handle: RePEc:eoh:wpaper:wpaper-002
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    References listed on IDEAS

    as
    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Stan Lipovetsky & W. Michael Conklin, 2015. "Predictor relative importance and matching regression parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1017-1031, May.
    3. Richard Johnson, 1966. "The minimal transformation to orthonormality," Psychometrika, Springer;The Psychometric Society, vol. 31(1), pages 61-66, March.
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