sim_parAcvf {pcts}

Create a random periodic autocovariance function

Description

Select randomly a periodic autoregression model and return the periodic autocovariances associated with it.

Usage

sim_parAcvf(period, order, sigma2)

Arguments

period

the period, a positive integer.

order

the AR order, a vector of non-negative integers.

sigma2

the variances of the innovations, a numeric vector of length period (todo: or one?).

Details

Uses sim_parCoef() to generate a random PAR model.

Value

an object of class "matrix". In addition, the specification of the model is in attribute "model" which is a list with the following components:

ar

a matrix, the coefficients of the PAR model,

sigma2

numeric, the innovation variances,

order

the PAR order.

Author(s)

Georgi N. Boshnakov

References

Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral properties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.1467-9892.2009.00617.x.

Examples

sim_parAcvf(2, 5)
sim_parAcvf(3, 5)

res <- sim_parAcvf(2, 6)
res
slMatrix(res)[3, 4, type = "tt"]

res <- sim_parAcvf(2, 4)
attr(res, "model")
acv <- res[ , ] # drop attributes

acv[2, 1 + 0]
acv[2, 1 + 1]
slMatrix(acv)[2, 0]
slMatrix(acv)[2, 1]
slMatrix(acv)[3, 4, type = "tt"]
slMatrix(acv)[1:2, 1:2, type = "tt"]
slMatrix(acv)[1:4, 1:4, type = "tt"]

## TODO: need method for autocorrelation()
## pc.acrf(acv)

## TODO: these need changing, after the change of the return values of sim_parAcvf
## pc.fcoeffs(acv, 2)
## pc.fcoeffs(acv, 3)
## pc.fcoeffs(acv, 4)
pcts:::calc_predictionCoefficients(acv, c(2, 2))
pcts:::calc_predictionCoefficients(acv, c(3, 3))
pcts:::calc_predictionCoefficients(acv, c(4, 4))