Summaryccm: Summary Statistics of Cross-Correlation Matrices

In SLBDD: Statistical Learning for Big Dependent Data

Summary Statistics of Cross-Correlation Matrices

Description

Compute and plot summary statistics of cross-correlation matrices (CCM) for high-dimensional time series.

Usage

Summaryccm(x, max.lag = 12)

Arguments

x	T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.
max.lag	The number of lags for CCM.

Value

A list containing:

• pvalue - P-values of Chi-square tests of individual-lag CCM being zero-matrix.

• ndiag - Percentage of significant diagonal elements for each lag.

• noff - Percentage of significant off-diagonal elements for each lag.

Examples

data(TaiwanAirBox032017)
output <- Summaryccm(as.matrix(TaiwanAirBox032017[,1:4]))

SLBDD documentation built on April 27, 2022, 5:08 p.m.