SCMid: Scalar Component Identification

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

Scalar Component Identification

Description

Find the overall order of a VARMA process via the scalar component model approach

Usage

SCMid(zt, maxp = 5, maxq = 5, h = 0, crit = 0.05, output = FALSE)

Arguments

zt	The T-by-k data matrix of a k-dimensional time series
maxp	Maximum AR order entertained. Default is 5.
maxq	Maximum MA order entertained. Default is 5.
h	The additional past lags used in canonical correlation analysis. Default is 0.
crit	Type-I error of the chi-square tests used.
output	A logical switch to control the output.

Value

Nmtx	The table of the numbers of zero canonical correlations
DDmtx	The diagonal difference table of the number of zero canonical correlations

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 4). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

phi=matrix(c(0.2,-0.6,0.3,1.1),2,2); sigma=diag(2)
m1=VARMAsim(300,arlags=c(1),phi=phi,sigma=sigma)
zt=m1$series
m2=SCMid(zt)

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