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.

(t,m,s)-net, Quasi-Monte Carlo, lattice points, multinomial probit, simulation
Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Discrete Regression and Qualitative Choice Models; Discrete Regressors (jel C35)
Econometric Institute Research Papers
Erasmus School of Economics

Sándor, Z, & András, P. (2003). Alternate Samplingmethods for Estimating Multivariate Normal Probabilities (No. EI 2003-05). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1690