parcovmatlist {pcts}

Compute asymptotic covariance matrix for PAR model

Description

Compute asymptotic covariance matrix for PAR model

Usage

parcovmatlist(parmodel, n, cor = FALSE, result = "list")

Arguments

parmodel

PAR model, object of class parModel

n

length of the series or a vector with one element for each season.

cor

If TRUE return correlation matrix.

result

if "list", the default, return a list, if "Matrix" return a Matrix object, otherwise return an ordinary matrix, see Details.

Details

Uses eq. (3.3) in the reference.

If result = "list", parcovmatlist returns a list whose s-th element is the covariance matrix of the PAR parameters for the s-th season. Otherwise, if result = "Matrix" it returns a block-diagonal matrix created by .bdiag() from package "Matrix". If result = "matrix" it returns an ordinary matrix (with the current implementation this is returned for any value other than "list" or "Matriix").

Value

a list, matrix or block-diagonal matrix, as described in Details

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

pcacfMat, pc.acf.parModel

Examples

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

parcovmatlist(proba1, 100)
parcovmatlist(proba1, 100, cor = TRUE)
sqrt(diag(parcovmatlist(proba1, 100, cor = TRUE)[[1]]))

meanvarcheck(proba1, 100)