Alternative sampling methods for estimating multivariate normal probabilities

S�ndor, Zsolt; Andr�s, P�ter

Abstract

We study the performance of alternative sampling methods for estimating multivariate normal probabilities through the GHK simulator. The sampling methods are randomized versions of some quasi-Monte Carlo samples (Halton, Niederreiter, Niederreiter–Xing sequences and lattice points) and some samples based on orthogonal arrays (Latin hypercube, orthogonal array and orthogonal array based Latin hypercube samples). In general, these samples turn out to have a better performance than Monte Carlo and antithetic Monte Carlo samples. Improvements over these are large for low-dimensional (4 and 10) cases and still significant for dimensions as large as 50.

Citation

Sándor, Z., & András, P. (2004). Alternative sampling methods for estimating multivariate normal probabilities. Journal of Econometrics, 120(2), 207-234. https://doi.org/10.1016/S0304-4076%2803%2900212-4

• Journal Article Type: Article
• Acceptance Date: Jun 30, 2003
• Online Publication Date: Sep 2, 2003
• Publication Date: 2004-06
• Deposit Date: Nov 4, 2021
• Journal: Journal of Econometrics
• Print ISSN: 0304-4076
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 120
• Issue: 2
• Pages: 207-234
• DOI: https://doi.org/10.1016/S0304-4076%2803%2900212-4
• Keywords: Simulation, Quasi-Monte Carlo, (t,m,s)-net, Lattice points, Multinomial probit
• Public URL: http://researchrepository.napier.ac.uk/Output/2808975