define_configurations

Constructs dataframe where the rows contains all configurations that are included and for which the estimators will be estimated.

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

define_configurations function

<dl><dt>S_cand</dt>
<dd>candidates for S (number of groups)</dd>
<dt>k_cand</dt>
<dd>candidates for k (number of common factors)</dd>
<dt>kg_cand</dt>
<dd>candidates for kg (number of group specific factors)</dd></dl>

Arguments

Constructs dataframe where the rows contains all configurations that are included and for which the estimators will be estimated. — define_configurations

<dl>

<dt>S_cand</dt>
<dd>candidates for S (number of groups)</dd>


<dt>k_cand</dt>
<dd>candidates for k (number of common factors)</dd>


<dt>kg_cand</dt>
<dd>candidates for kg (number of group specific factors)</dd>

</dl>

define_configurations: Constructs dataframe where the rows contains all configurations that are included and for which the estimators will be estimated.

Description

Usage

Value

Arguments

Examples