Computational Challenges in Vector Functional Coefficient Autoregressive Models

Banicescu, I., Carino, R.L., Harvill, J. L., & Lestrade, J. P. (2005). Computational Challenges in Vector Functional Coefficient Autoregressive Models. In V.S. Sunderam et al. (Eds.), Lecture Notes in Computer Science. Atlanta, GA: Springer. 3514, 237-244.

An important research area in statistical computing is found in the literature for vector functional coefficient autoregressive models, a special case of vector nonlinear time series. Methods used are computationally intensive. As a result, analyses and simulations can run into weeks, or even months. Statisticians have been known to base empirical results on a relatively small number of simulation replications, sacrificing precision, accuracy 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, without resorting to extensive code revisions. This approach takes advantage of recent advances in dynamic loop scheduling on workstation clusters to achieve high performance, 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 8–64 processors.