solveFG

Helpfunction in update_g(), to calculate solve(FG x t(FG)) x FG

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework.
The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta.
This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
with their loadings. The number of groups and factors can be estimated if they are unknown.

Ewoud Heyndels

RCTS

Clustering Time Series While Resisting Outliers

solveFG function

<dl><dt>TT</dt>
<dd>length of time series</dd>
<dt>S</dt>
<dd>number of groups</dd>
<dt>kg</dt>
<dd>vector with the estimated number of group specific factors for each group</dd>
<dt>factor_group</dt>
<dd>estimated group specific factors</dd>
<dt>testing</dt>
<dd>variable that determines if we are in 'testing phase'; defaults to FALSE (requires Matrix-package if set to TRUE)</dd></dl>

Arguments

Helpfunction in update_g(), to calculate solve(FG x t(FG)) x FG — solveFG

<dl>

<dt>TT</dt>
<dd>length of time series</dd>


<dt>S</dt>
<dd>number of groups</dd>


<dt>kg</dt>
<dd>vector with the estimated number of group specific factors for each group</dd>


<dt>factor_group</dt>
<dd>estimated group specific factors</dd>


<dt>testing</dt>
<dd>variable that determines if we are in 'testing phase'; defaults to FALSE (requires Matrix-package if set to TRUE)</dd>

</dl>

solveFG: Helpfunction in update_g(), to calculate solve(FG x t(FG)) x FG

Description

Usage

Value

Arguments