HPC MSU

Publication Abstract

Investigating Asympotic Properties of Vector Nonlinear Time Series Models

Banicescu, I., Carino, R.L., Harvill, J. L., & Lestrade, J. P. (2011). Investigating Asympotic Properties of Vector Nonlinear Time Series Models. Journal of Computational and Applied Mathematics. Elsevier. 236(3), 411-421.

Abstract

Analyses and simulations of vectornonlineartimeseries typically run into weeks or even months because the methods used are computationally intensive. Statisticians have been known to base empirical results on a relatively small number of simulation replications, sacrificing precision and reliability of results in the interest of time and productivity. The simulations are amenable for parallelization. However, parallel computing technology has not yet been widely used in this specific research area. This paper proposes an approach to the parallelization of statistical simulation codes to address the challenge of long running times. Requiring minimal code revision, this approach takes advantage of recent advances in dynamic loop scheduling to achieve high performance on general-purpose clusters, even with the presence of unpredictable load imbalance factors. Preliminary results of applying this approach in the simulation of normal white noise and threshold autoregressive model obtains efficiencies in the range 95%98% on 864 processors. Furthermore, previously unobserved properties of the statistical procedures for the models are uncovered by the simulation.