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

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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.

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Authors and Affiliations

  1. Department of Chemical Engineering, Queen’s University, Kingston, Ontario, K7L 3N6, Canada

    T. J. Harris

  2. Industrial Information & Control Centre, The University of Auckland, Auckland, New Zealand

    W. Yu

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  1. T. J. Harris
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  2. W. Yu
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Correspondence to T. J. Harris.

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Harris, T.J., Yu, W. Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis. Stat Comput 22, 387–396 (2012). https://doi.org/10.1007/s11222-011-9230-7

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