The function mcov computes estimates of the lag l moving cross-covariance matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the mcov function: library(roll

Alshammri, Fayed Awdah M

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University of Strathclyde Institutional Repository

3/10/2020 R: Moving Cross-covariance Matrix127.0.0.1:20306/library/mcov/html/mcov.html 1/1mcov {mcov} R DocumentationMoving Cross-covariance MatrixDescriptionThe function mcov computes estimates of the lag l moving cross-covariance matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the mcov function:library(roll)Usagemcov(x,w,l)Argumentsxa T-by-m data matrix, where the rows are "T" time points, and the columns are "m" variableswwindow width (i.e. window size) at which the mcov is calculatedlthe lag at which the mcov is calculatedValuemcova symmetric m-by-m matrix, which is the lag l moving cross-covariance matrix of the data matrix xNoteChoosing the window width (i.e. w) is vital to enhance the results of the mcov to extract accurate information from the data. The size of w depends on the degree of stationarity of the data. Small window sizes are suitable for datathat exhibit strong non-stationarity. For stationary data, a window of size "w=T-l" is used.Author(s)Fayed AlshammriReferencesAlshammri, F. and Pan, J. (2019). Moving dynamic principal component analysis for non-stationary multivariate time series. Manuscript submitted for publication.Examples##The data matrix X is a non-stationary time series with m=6 and T=1500. m=6;T=1500 # Generate x_t X=mat.or.vec(m,T) a1=arima.sim(list(order=c(1,1,1),ar=0.75,ma=0.9),n=T+1,sd=1) for(i in 1:2) X[i,]=a1[i+1:T] a2=arima.sim(list(order=c(1,1,1),ar=0.6,ma=-1.4),n=T+1,sd=1) for(i in 3:4) X[i,]=a2[(i-1):(T+i-2)] a3=arima.sim(list(order=c(1,1,1),ar=-0.7,ma=-2.3),n=T+1,sd=1) for(i in 5:6) X[i,]=a3[(i-3):(T+i-4)] X=t(X) X=ts(X) ##calculate the lag l moving cross-covariance of x, with w=100 and l=2. myresult=mcov(X,100,2) myresult [Package mcov version 0.1.0 Index]

mcov : Moving Cross-covariance Matrix

https://strathprints.strath.ac.uk/71855/1/Alshammri_2020_mcov_Moving_Cross_covariance_Matrix.pdf

mcov : Moving Cross-covariance Matrix

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