Eccm: Extended Cross-Correlation Matrices

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Extended Cross-Correlation Matrices

Description

Compute the extended cross-correlation matrices and the associated two-way table of p-values of multivariate Ljung-Box statistics of a vector time series.

Usage

Eccm(zt, maxp = 5, maxq = 6, include.mean = FALSE, rev = TRUE)

Arguments

zt	Data matrix (T-by-k) of a vector time series, where T is the sample size and k is the dimension.
maxp	Maximum AR order entertained. Default is 5.
maxq	Maximum MA order entertained. Default is 6.
include.mean	A logical switch controlling the mean vector in estimation. Default assumes zero mean.
rev	A logical switch to control the cross-correlation matrices used to compute the multivariate Ljung-Box statistics. Traditional way is to compute test statistics from lag-1 to lag-m. If rev = TRUE, then the test statistics are compute from lag-(m-1) to lag-m, from lag-(m-2) to lag-m, etc.

Value

pEccm	A two-way table of the p-values of extended cross-correlation matrices
vEccm	The sample extended cross-correlation matrices
ARcoef	AR coefficient matrices of iterated VAR fitting

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

zt=matrix(rnorm(900),300,3)
m1=Eccm(zt)

MTS documentation built on April 11, 2022, 5:07 p.m.