crossdep_3series: Cross-dependence statistics for testing independence between...

In IndGenErrors: Tests of Independence Between Innovations of Generalized Error Models

Cross-dependence statistics for testing independence between the innovations of 3 series of same length

Description

This function computes the cross-dependence for Spearman, van der Waerden and Savage dependence measures, for all lags = -lag2, .. lag2, for all pairs, and for pair of lags = (-lag3,-lag3),...(lag3,lag3) for the three series.

Usage

crossdep_3series(x, y, z, lag2, lag3)

Arguments

x	Pseudo-observations (or residuals) of first series.
y	Pseudo-observations (or residuals) of second series.
z	Pseudo-observations (or residuals) of third series.
lag2	Maximum number of lags around 0 for pairs of series.
lag3	Maximum number of lags around 0 for the three series.

Value

stat	Cross-dependences for all lags and for all subsets
H	Sum of squares of cross-correlations for all subsets
pvalue	P-value of LB for all subsets and H
n	length of the time series

References

Duchesne, Ghoudi & Remillard (2012). On Testing for independence between the innovations of several time series. CJS, vol. 40, 447-479.

Nasri & Remillard (2024). Tests of independence and randomness for arbitrary data using copula-based covariances. JMVA, vol. 201, 105273.

Examples

#Romano-Siegel's example #
data(romano_ex)
outr = crossdep_3series(romano_ex$x,romano_ex$y,romano_ex$z,5,2)
CrossCorrelogram(outr$spearman$out123,"Savage for {1,2,3}",rot=90)

IndGenErrors documentation built on April 3, 2025, 9:09 p.m.