pc.hat.h

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.

pcarma

Classes and methods for modelling and simulation of
periodically correlated (PC) and periodically integrated time
series. Compute theoretical periodic autocovariances and related
properties of PC autoregressive moving average models. Some original
methods including Boshnakov & Iqelan (2009)
<doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
<doi:10.1111/j.1467-9892.1996.tb00281.x>.

Georgi Boshnakov

pcts

Periodically Correlated and Periodically Integrated Time Series

pc.hat.h function

<dl> <dt>x</dt>
<dd>the observed time series x(t)</dd> <dt>eps</dt>
<dd>a series of esimated innovations</dd> <dt>maxlag</dt>
<dd>maximum lag</dd> <dt>si2hat</dt>
<dd>estimate of the variance of the innovations</dd></dl>

Arguments

Author

function to compute estimates of the h weights — pc.hat.h

<dl>

 <dt>x</dt>
<dd>the observed time series x(t)</dd>

 <dt>eps</dt>
<dd>a series of esimated innovations</dd>

 <dt>maxlag</dt>
<dd>maximum lag</dd>

 <dt>si2hat</dt>
<dd>estimate of the variance of the innovations</dd>

</dl>

pc.hat.h: function to compute estimates of the h weights

Description

Usage

Value

Arguments

Author

Details

References