Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis

Authors:
T. J. Harris;W. Yu
Affiliations:
Department of Chemical Engineering, Queen's University, Kingston, Canada K7L 3N6;Industrial Information & Control Centre, The University of Auckland, Auckland, New Zealand
Venue:
Statistics and Computing
Year:
2012

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Abstract

In this paper, A variance decomposition approach to quantify the effects of endogenous and exogenous variables for nonlinear time series models is developed. This decomposition is taken temporally with respect to the source of variation. The methodology uses Monte Carlo methods to affect the variance decomposition using the ANOVA-like procedures proposed in Archer et al. (J. Stat. Comput. Simul. 58:99---120, 1997), Sobol' (Math. Model. 2:112---118, 1990). The results of this paper can be used in investment problems, biomathematics and control theory, where nonlinear time series with multiple inputs are encountered.