Further evidence on sparse grids-based numerical integration in the mixed logit model

Zsolt Sándor ()
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Zsolt Sándor: Sapientia Hungarian University of Transylvania

Economics Bulletin, 2019, vol. 39, issue 4, 2726-2731

Abstract: We study the performance of Gauss quadrature methods based on sparse grids for approximating integrals involved in mixed logit models. In Monte Carlo experiments we consider data generating processes in which consumer heterogeneity has low variance and data generating processes in which it has high variance. In the former case we find that, in line with previous literature, sparse grids produce very accurate estimates even when the number of points used for approximating integrals is small. However, in the latter case sparse grids yield biased estimates and are outperformed by quasi-Monte Carlo methods.

Keywords: discrete choice; random coefficients; simulation; quasi-Monte Carlo; panel (search for similar items in EconPapers)
JEL-codes: C1 C6 (search for similar items in EconPapers)
Date: 2019-11-29
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