Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood

Peter Haan and Arne Uhlendorff

No 573, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: 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. The advantage of simulation over Gauss Hermite quadrature is a marked reduction in computational time for integrals with higher dimensions. Bayesian quadrature, however, leads to very stable results with only a few quadrature points, thus the computational advantage of Halton based simulation vanishes in our example with one and two dimensional integrals.

Keywords: multinomial logit model; panel data; unobserved heterogeneity; maximum simulated likelihood; Halton sequences (search for similar items in EconPapers)
Pages: 16 p.
Date: 2006
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (52)

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Journal Article: Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood (2006)
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