Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis. Harris, T. J. & Yu, W. Statistics and Computing, 22(2):387–396, March, 2012.
Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis [link]Paper  doi  abstract   bibtex   
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.
@article{harris_variance_2012,
	title = {Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis},
	volume = {22},
	issn = {1573-1375},
	url = {https://doi.org/10.1007/s11222-011-9230-7},
	doi = {10.1007/s11222-011-9230-7},
	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.},
	language = {en},
	number = {2},
	urldate = {2020-06-02},
	journal = {Statistics and Computing},
	author = {Harris, T. J. and Yu, W.},
	month = mar,
	year = {2012},
	pages = {387--396}
}

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