pc.hat.h {pcts}

function to compute estimates of the h weights

Description

The h coefficients are scaled cross-covariances between the time series and the innovations. This function computes estimates for h using as input the observed series, a series of estimated innovations, and an estimate of the variance of the innovations.

Usage

pc.hat.h(x, eps, maxlag, si2hat)

Arguments

x

the observed time series x(t)

eps

a series of esimated innovations

maxlag

maximum lag

si2hat

estimate of the variance of the innovations

Details

If missing, the variance of the innovations is estimated from eps.

Value

A matrix of the coefficient up to lag maxlag with one row for each season.

Author(s)

Georgi N. Boshnakov

References

Boshnakov GN (1996). “Recursive computation of the parameters of periodic autoregressive moving-average processes.” J. Time Ser. Anal., 17(4), 333–349. ISSN 0143-9782, doi:10.1111/j.1467-9892.1996.tb00281.x.