pcacfMat {pcts}

Compute PAR autocovariance matrix

Description

Compute PAR autocovariance matrix

Usage

pc.acf.parModel(parmodel, maxlag = NULL)

pcacfMat(parmodel)

Arguments

parmodel

PAR model, an object of class parModel.

maxlag

maximum lag

Details

pc.acf.parModel returns the autocovariances of a PAR model in season-lag form with maximum lag equal to maxlag. If maxlag is larger than the available precomputed autocovariances, they missing ones are computed using the Yule-Walker relations. Note that pc.acf.parModel assumes that there are enough precomputed autocovariances to use the Yule-Walker recursions directly.

TODO: pc.acf.parModel is tied to the old classes since it accesses their slots. Could be used as a template to streamline the method for autocovariances for class "PeriodicAutocovariance".

The season-lag form can be easily converted to other forms with the powerful indexing operator, see the examples and slMatrix-class.

pcacfMat is a convenience function for statistical inference. It creates a covariance matrix with dimension chosen automatically. This covariance matrix is such that the asymptotic covariance matrix of the estimated parameters can be obtained by dividing sub-blocks by innovation variances and inverting them. See, eq. (3.3) in the reference.

Value

for pcacfMat, a matrix

for pc.acf.parModel, an slMatrix

Author(s)

Georgi N. Boshnakov

References

McLeod AI (1994). “Diagnostic checking of periodic autoregression models with application.” Journal of Time Series Analysis, 15(2), 221–233.

See Also

slMatrix-class

Examples

x <- arima.sim(list(ar = 0.9), n = 1000)
proba1 <- fitPM(c(3,2,2,2), x)

acfb <- pc.acf.parModel(proba1, maxlag = 8)
acfb[4:(-2), 4:(-2), type = "tt"]

pcacfMat(proba1)