<abstract xmlns="http://www.w3.org/1999/xhtml"><p>We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate integrals. Such large sums occur in the random coefficient logit with demographic characteristics, the random coefficient logit with limited consumer information and the design of choice experiments for the panel mixed logit. We show that certain quasi-Monte Carlo methods, that is, so-called (t, m, s)-nets, provide improved performance over pseudo-Monte Carlo methods in terms of bias, standard deviation and root mean squared error.</p></abstract>

We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate integrals. Such large sums occur in the random coefficient logit with demographic characteristics, the random coefficient logit with limited consumer information and the design of choice experiments for the panel mixed logit. We show that certain quasi-Monte Carlo methods, that is, so-called (t, m, s)-nets, provide improved performance over pseudo-Monte Carlo methods in terms of bias, standard deviation and root mean squared error.

Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums

Sapientia Hungarian University of Transylvania Faculty of Economic and Human Sciences, Miercurea Ciuc

Acta Universitatis Sapientiae, Economics and Business

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums"}

We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate...

Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums

Online veröffentlicht: 30. Mai 2014

Seitenbereich: 85 - 108

DOI: https://doi.org/10.2478/auseb-2014-0006

© 2014

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.