forecastResidual

Forecasts the residual value of a VAR realization given the white noise covariance matrix

S3 and S4 functions are implemented for spatial multi-site
stochastic generation of daily time series of temperature and
precipitation. These tools make use of Vector AutoRegressive models (VARs).
The weather generator model is then saved as an object and is calibrated by
daily instrumental "Gaussianized" time series through the 'vars' package
tools. Once obtained this model, it can it can be used for weather
generations and be adapted to work with several climatic monthly time
series.

Emanuele Cordano

RMAWGEN

Multi-Site Auto-Regressive Weather GENerator

Emanuele Eccel

Eike Luedeling

forecastResidual function

<dl><dt>var</dt>
<dd>A VAR model represented by a <code>varest</code> object as returned by <code>getVARmodel</code> or <code>VAR</code></dd>
<dt>xprev</dt>
<dd>previous status of the random variable, in this case the "current instant"white-noise". Default is <code>NULL</code> and then randomly generated.</dd>
<dt>B</dt>
<dd>matrix of coefficients for the vectorial white-noise component</dd></dl>

Arguments

Author

Forecasts the residual value of a VAR realization given the white noise covariance matrix — forecastResidual

<dl>

<dt>var</dt>
<dd>A VAR model represented by a <code>varest</code> object as returned by <code>getVARmodel</code> or <code>VAR</code></dd>


<dt>xprev</dt>
<dd>previous status of the random variable, in this case the "current instant"white-noise". Default is <code>NULL</code> and then randomly generated.</dd>


<dt>B</dt>
<dd>matrix of coefficients for the vectorial white-noise component</dd>

</dl>

forecastResidual: Forecasts the residual value of a VAR realization given the white noise covariance matrix

Description

Usage

Value

Arguments

Author

See Also