VarianceRacfARz: Covariance Matrix Residual Autocorrelations for ARz

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

Description

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.

Usage

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VarianceRacfARz(zeta, lags, MaxLag, n)

Arguments

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

Details

The covariance matrix of the residual autocorrelations in the subset ARz case is derived in McLeod and Zhang (2006, eqn. 16)

Value

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

Author(s)

A.I. McLeod and Y. Zhang

References

McLeod, A.I. and Zhang, Y. (2006). Partial autocorrelation parameterization for subset autoregression. Journal of Time Series Analysis, 27, 599-612.

See Also

VarianceRacfAR, VarianceRacfARz, RacfPlot

Examples

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#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))

FitAR documentation built on May 2, 2019, 3:22 a.m.