kg_candidates_expand

Function that returns the set of combinations of groupfactors for which the algorithm needs to run.

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

kg_candidates_expand function

<dl><dt>S</dt>
<dd>number of groups</dd>
<dt>kg_min</dt>
<dd>minimum value for number of group specific factors</dd>
<dt>kg_max</dt>
<dd>minimum value for number of group specific factors</dd>
<dt>limit_est_groups</dt>
<dd>maximum allowed number of groups that can be estimated</dd></dl>

Arguments

Function that returns the set of combinations of groupfactors for which the algorithm needs to run. — kg_candidates_expand

<dl>

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


<dt>kg_min</dt>
<dd>minimum value for number of group specific factors</dd>


<dt>kg_max</dt>
<dd>minimum value for number of group specific factors</dd>


<dt>limit_est_groups</dt>
<dd>maximum allowed number of groups that can be estimated</dd>

</dl>

kg_candidates_expand: Function that returns the set of combinations of groupfactors for which the algorithm needs to run.

Description

Usage

Value

Arguments