Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums

Sándor Zsolt ()
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Sándor Zsolt: Sapientia Hungarian University of Transylvania Faculty of Economic and Human Sciences, Miercurea Ciuc

Acta Universitatis Sapientiae, Economics and Business, 2013, vol. 1, issue 1, 85-108

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.

Keywords: BLP; (0; m; s)-nets; lattice points; conjoint choice design (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:auseab:v:1:y:2013:i:1:p:85-108:n:6

DOI: 10.2478/auseb-2014-0006

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