add_configuration

Adds the current configuration (number of groups and factors) to df_results.

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

add_configuration function

<dl><dt>df_results</dt>
<dd>dataframe with results for each estimated configuration</dd>
<dt>S</dt>
<dd>estimated number of groups in current configuration</dd>
<dt>k</dt>
<dd>estimated number of common factors in current configuration</dd>
<dt>kg</dt>
<dd>vector with the estimated number of group specific factors in current configuration (augmented with NA's to reach a length of 20)</dd></dl>

Arguments

Adds the current configuration (number of groups and factors) to df_results. — add_configuration

<dl>

<dt>df_results</dt>
<dd>dataframe with results for each estimated configuration</dd>


<dt>S</dt>
<dd>estimated number of groups in current configuration</dd>


<dt>k</dt>
<dd>estimated number of common factors in current configuration</dd>


<dt>kg</dt>
<dd>vector with the estimated number of group specific factors in current configuration (augmented with NA's to reach a length of 20)</dd>

</dl>

add_configuration: Adds the current configuration (number of groups and factors) to df_results.

Description

Usage

Value

Arguments

Examples