VarianceRacfARp(FitAR)R Documentation

Covariance matrix residual autocorrelations subset AR-phi

Description

The AR-phi 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.

Usage

VarianceRacfARp(phi, lags, MaxLag, n)

Arguments

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

Details

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.

Value

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

Author(s)

A.I. McLeod

References

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.

See Also

VarianceRacfAR, VarianceRacfARz, RacfPlot

Examples

#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))

[Package FitAR version 1.0 Index]