predict_empirical

Prediction function for tscm class with empirical margin

internal

Functions for the analysis of time series using copula models.
The package is based on methodology described in the following references.
McNeil, A.J. (2021) <doi:10.3390/risks9010014>,
Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>,
Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Alexander McNeil

tscopula

Time Series Copula Models

Martin Bladt

predict_empirical function

<dl><dt>object</dt>
<dd>an object of the class.</dd>
<dt>data</dt>
<dd>vector of past data values.</dd>
<dt>x</dt>
<dd>vector of arguments of prediction function.</dd>
<dt>type</dt>
<dd>type of prediction function ("df" for density, "qf" for quantile function
or "dens" for density).</dd>
<dt>qtype</dt>
<dd>type of empirical quantile estimate.</dd>
<dt>proper</dt>
<dd>logical variable stating whether the standard empirical distribution function should
be used when the margin is empirical; otherwise an improper distribution that is bounded away from 0
and 1 is used.</dd></dl>

Arguments

Prediction function for tscm class with empirical margin — predict_empirical

<dl>

<dt>object</dt>
<dd>an object of the class.</dd>


<dt>data</dt>
<dd>vector of past data values.</dd>


<dt>x</dt>
<dd>vector of arguments of prediction function.</dd>


<dt>type</dt>
<dd>type of prediction function ("df" for density, "qf" for quantile function
or "dens" for density).</dd>


<dt>qtype</dt>
<dd>type of empirical quantile estimate.</dd>


<dt>proper</dt>
<dd>logical variable stating whether the standard empirical distribution function should
be used when the margin is empirical; otherwise an improper distribution that is bounded away from 0
and 1 is used.</dd>

</dl>

predict_empirical: Prediction function for tscm class with empirical margin

Description

Usage

Arguments