pclspiar: Fit a periodically integrated autoregressive model

In GeoBosh/pcts: Periodically Correlated and Periodically Integrated Time Series

Fit a periodically integrated autoregressive model

Description

Fit a periodically integrated autoregressive model.

Usage

pclspiar(x, d, p, icoef = NULL, parcoef = NULL, sintercept = FALSE,
         seasonof1st = 1, weights = TRUE, itol = 1e-07, maxniter = 1000)

Arguments

x	time series.
d	period.
p	order of the model, a positive integer, see Details.
icoef	initial values for the periodic integration coefficients. If missing or NULL suitable values are computed.
parcoef	not used currently.
sintercept	if TRUE include seasonal intercepts.
seasonof1st	season of the first observation.
weights	if TRUE, use periodic weights in the nonlinear least squares, see Details.
itol	threshold value for the stopping criterion.
maxniter	maximum number of iterations.

Details

This function fits a periodically integrated autoregressive model using non-linear least squares. The order of integration is one and the order of the periodically correlated part is p - 1. So, p must be greater than or equal to one.

If weights = TRUE the non-linear optimisation is done with weights inversely proportional to the innovation variances for the seasons, otherwise the unweighted sum of squared residuals is minimised.

Value

a list currently containing the following elements:

icoef	coefficients of the periodic integration filter.
parcoef	coefficients of the PAR filter.
sintercept	seasonal intercepts.
sigma2hat	innovation variances.

Author(s)

Georgi N. Boshnakov

References

\insertRef

FransesB1pcts

\insertRef

FransesB2pcts

\insertRef

boshnakov2009genpcts

See Also

pclsdf, test_piar, fitPM

Examples

## see also the examples for fitPM()
ts1 <- window(dataFranses1996[ , "CanadaUnemployment"],
              start = c(1960, 1), end = c(1987, 4))
pclspiar(ts1, 4, p = 1, sintercept = TRUE)
pclspiar(ts1, 4, p = 2, sintercept = TRUE)

GeoBosh/pcts documentation built on Dec. 8, 2023, 9:57 p.m.