function to compute estimates of the h weights

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.

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 .

Author

Georgi N. Boshnakov