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Multinomial Logit Models
Multinomial Logit Models

Publikationen des DIW Berlin

The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. In this paper we are proposing a sparsity-inducing penalty that accounts for the special structure of multinomial models. In contrast to existing methods, it penalizes the parameters that are linked to one variable in a grouped way and thus yields variable selection instead of parameter selection. We develop a proximal gradient method that is able to efficiently compute stable estimates. In addition, the penalization is extended to the important case of predictors that vary across response categories. We apply our estimator to the modeling of party choice of voters in Germany including voter-specific variables like age and gender but also party-specific features like stance on nuclear energy and immigration.

In this paper, we suggest a Stata routine for multinomial logit models. The purpose of this paper is twofold. First, we describe the. Our empirical findings. The advantage of simulation over Gauss-Hermite quadrature is a marked.

In this paper we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold: First, we provide a description of the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine to the Stata program gllamm which solves integration using Gauss Hermite quadrature or Bayesian adaptive quadrature. For the analysis we draw on multilevel data about schooling. Our empirical findings show that the estimation techniques lead to approximately the same estimation results.

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated. Please use this identifier to cite or link to this item: In this paper we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold: First, we provide a description of the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine to the Stata program gllamm which solves integration using Gauss Hermite quadrature or Bayesian adaptive quadrature. For the analysis we draw on multilevel data about schooling.

Bibtex-Export Endnote-Export. Abstract Fixed effect models have become increasingly popular in the field of sociology. The possibility to control for unobserved heterogeneity makes these models a prime tool for causal analysis. As of today, fixed effects models have been derived and implemented for many statistical software packages for Fixed effect models have become increasingly popular in the field of sociology.

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Multinomial Logit Models

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In this paper we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton . 1. Okt. Tutz, Gerhard; Pößnecker, Wolfgang; Uhlmann, Lorenz ( Juni ): Variable Selection in General Multinomial Logit Models. Department of. Körperschaftlicher Herausgeber GESIS - Leibniz-Institut für Sozialwissenschaften . Abstract. Fixed effect models have become increasingly popular in the field of.
Multinomial Logit Models

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Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood Peter Haan, Arne Uhlendorff Apr. 16 S. Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood Peter Haan, Arne Uhlendorff In: The Stata Journal. The multinomial logistic regression model is defined by the following assumptions: ▻ Observations Yi are statistically independent of each.

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Multinomial Logit Models

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