icss: Iterative Cumulative Sum of Squares Algorithm

In micss: Modified Iterative Cumulative Sum of Squares Algorithm

Iterative Cumulative Sum of Squares Algorithm

Description

Implements the ICSS algorithm of Inclan and Tiao (1994) using the CUMSUMQ test detailed in Carrion-i-Silvestre & Sansó (2023)

Usage

icss(e,sig.lev=0.05,kmax=NULL,alpha=NULL)

Arguments

e	A numeric vector. Stationary variable on which the constancy of unconditional variance is tested.
sig.lev	Significance level. The default value is 0.05
kmax	Maximum lag to be used for the estimation of the long-run fourth order moment. If not reported, an automatic procedure computes it depending on the sample size.
alpha	Tail index. If not reported, it is set at 4, which was the implicit assumption of Inclan & Tiao (1994). Values between 2 and 4 are allowed because this function is used by micss.

Value

nb	Number of breaks found by the algorithm.
tb	Vector with the time breaks.

Author(s)

J.L. Carrion-i-Silvestre and A. Sanso.

References

J.L. Carrion-i-Silvestre & A. Sansó (2023): Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series.

C. Inclan & G.C. Tiao (1994): Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance. Journal of the American Statistical Association 89, 913-923.

See Also

micss print.icss plot.icss

Examples

set.seed(2)
e <- c(stats::rnorm(200),3*stats::rnorm(200))
o <- icss(e)
print.icss(o)

micss documentation built on Sept. 11, 2024, 6:46 p.m.