pcts (version 0.15.8)

pclspiar: 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)

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.

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.

Author

Georgi N. Boshnakov

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.

References

FransesB1pcts

FransesB2pcts

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)