acfM {dse1}R Documentation

Calculate Auto-Covariance

Description

Calculate a matrix with partitions [M0|...|Mi|...|Ml] giving the auto-covariance.

Usage

    acfM(obj, ...)
    acfM(obj, lag=round(6*log(periods(obj))), 
        type ="covariance", sub.mean=TRUE, ...)
    acfM(obj, lag=NULL, type ="covariance", Psi=NULL, ...)
    acfM(obj, ...)

Arguments

obj An object of class TSdata or TSmodel.
lag Number of lags for which to calculate the autocorrelations.
type With the defaults the blocks are auto-covariances. If type == 'correlation' the result is scaled to give autocorrelations.
sub.mean Only valid if object is of class TSdata. If FALSE then means are not subtracted.
Psi A matrix of innovation covariance. Only valid if object is of class TSmodel.
... arguments passed to other methods.

Value

A matrix with partitions [M0|...|Mi|...|Ml] giving the covariance or correlation, including the that between the output and input series (as in the first block row of a Hankel matrix).

Examples

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    z <- acfM(eg1.DSE.data.diff)
    model <- TSmodel(toSS(estVARXls(eg1.DSE.data.diff)))
    #  z <- acfM(model) not working

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