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Fitting Complex Mixed Logit Models with Particular Focus on Labor Supply Estimation

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  • Max Löffler

    (IZA, Bonn)

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    Abstract

    When one estimates discrete choice models, the mixed logit approach is commonly superior to simple conditional logit setups. Mixed logit models not only allow the researcher to implement difficult random components but also overcome the restrictive IIA assumption. Despite these theoretical advantages, the estimation of mixed logit models becomes cumbersome when the model’s complexity increases. Applied works therefore often rely on rather simple empirical specifications because this reduces the computational burden. I introduce the user-written command lslogit, which fits complex mixed logit models using maximum simulated likelihood methods. As lslogit is a d2-ML-evaluator written in Mata, the estimation is rather efficient compared with other routines. It allows the researcher to specify complicated structures of unobserved heterogeneity and to choose from a set of frequently used functional forms for the direct utility function—for example, Box-Cox transformations, which are difficult to estimate in the context of logit models. The particular focus of lslogit is on the estimation of labor supply models in the discrete choice context; therefore, it facilitates several computationally exhausting but standard tasks in this research area. However, the command can be used in many other applications of mixed logit models as well.

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    File URL: http://fmwww.bc.edu/RePEc/dsug2013/loeffler_DESUG2013.pdf
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    Bibliographic Info

    Paper provided by Stata Users Group in its series German Stata Users' Group Meetings 2013 with number 07.

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    Date of creation: 03 Jul 2013
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    Handle: RePEc:boc:dsug13:07

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    Web page: http://www.stata.com/meeting/germany13/
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    1. Hoynes, Hilary Williamson, 1996. "Welfare Transfers in Two-Parent Families: Labor Supply and Welfare Participation under AFDC-UP," Econometrica, Econometric Society, vol. 64(2), pages 295-332, March.
    2. Arthur van Soest, 1995. "Structural Models of Family Labor Supply: A Discrete Choice Approach," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 63-88.
    3. Peter Haan & Arne Uhlendorff, 2006. "Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood," Discussion Papers of DIW Berlin 573, DIW Berlin, German Institute for Economic Research.
    4. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
    5. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
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