Covariance Matrix Residual Autocorrelations for ARz
The ARz subset model is defined by taking a subset of the
partial autocorrelations (zeta parameters) in the 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.
VarianceRacfARz(zeta, lags, MaxLag, n)
zeta |
zeta parameters (partial autocorrelations) |
lags |
lags in model |
MaxLag |
covariance matrix for residual autocorrelations at lags 1,...,m, where m=MaxLag is computes |
n |
length of time series |
The covariance matrix of the residual autocorrelations in the subset ARz case is derived in McLeod and Zhang (2006, eqn. 16)
The m-by-m covariance matrix of residual autocorrelations at lags 1,...,m, where m = MaxLag.
A.I. McLeod and Y. Zhang
McLeod, A.I. and Zhang, Y. (2006). Partial autocorrelation parameterization for subset autoregression. Journal of Time Series Analysis, 27, 599-612.
#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))
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