VarianceRacfARp: Covariance Matrix Residual Autocorrelations for ARp
In FitAR: Subset AR Model Fitting

Description Usage Arguments Details Value Author(s) References See Also Examples

The ARp subset model is defined by taking a subset of the parameters in the regular AR(p) model. With this function one can obtain the standard deviations of the residual autocorrelations which can be used for diagnostic checking with RacfPlot.

1	VarianceRacfARp(phi, lags, MaxLag, n)

`phi`	vector of AR coefficients
`lags`	lags in subset AR
`MaxLag`	covariance matrix for residual autocorrelations at lags 1,...,m, where m=MaxLag is computes
`n`	length of time series

The covariance matrix for the residual autocorrelations is derived in McLeod (1978, eqn. 15) for the general ARMA case. McLeod (1978, eqn. 35) specializes this result to the subset case.

The m-by-m covariance matrix of residual autocorrelations at lags 1,...,m, where m = MaxLag.

A.I. McLeod

McLeod, A.I. (1978), On the distribution and applications of residual autocorrelations in Box-Jenkins modelling, Journal of the Royal Statistical Society B, 40, 296-302.

VarianceRacfAR, VarianceRacfARz, RacfPlot

#the standard deviations of the first 5 residual autocorrelations
#to a subset AR(1,2,6) model fitted to Series A is
v<-VarianceRacfARp(c(0.36,0.23,0.23),c(1,2,6), 5, 197)
sqrt(diag(v))