Fitting Complex Mixed Logit Models with Particular Focus on Labor Supply Estimation
AbstractWhen estimating 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 as 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 to 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---e.g., including 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 and therefore it facilitates several computational 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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Bibliographic InfoPaper provided by Stata Users Group in its series 2013 Stata Conference with number 8.
Date of creation: 01 Aug 2013
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-08-05 (All new papers)
- NEP-DCM-2013-08-05 (Discrete Choice Models)
- NEP-UPT-2013-08-05 (Utility Models & Prospect Theory)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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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