restructure_X_to_order_slowN_fastT

Restructures X (which is an 3D-array of dimensions (N,T,p) to a 2D-matrix of dimension (NxT,p).

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

restructure_X_to_order_slowN_fastT function

<dl><dt>X</dt>
<dd>input</dd>
<dt>vars_est</dt>
<dd>number of variables that will be included in the algorithm and have their coefficient estimated. This is usually equal to the number of observable variables.</dd></dl>

Arguments

Restructures X (which is an 3D-array of dimensions (N,T,p) to a 2D-matrix of dimension (NxT,p). — restructure_X_to_order_slowN_fastT

<dl>

<dt>X</dt>
<dd>input</dd>


<dt>vars_est</dt>
<dd>number of variables that will be included in the algorithm and have their coefficient estimated. This is usually equal to the number of observable variables.</dd>

</dl>

restructure_X_to_order_slowN_fastT: Restructures X (which is an 3D-array of dimensions (N,T,p) to a 2D-matrix of dimension (NxT,p).

Description

Usage

Value

Arguments