define_object_for_initial_clustering_macropca

This is a short version of define_object_for_initial_clustering() which only contains implementations for robust macropca case and classical case.

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_object_for_initial_clustering_macropca function

<dl><dt>Y</dt>
<dd>Y: NxT dataframe with the panel data of interest</dd>
<dt>k</dt>
<dd>number of common factors to be estimated</dd>
<dt>kg</dt>
<dd>number of group specific factors to be estimated</dd>
<dt>comfactor</dt>
<dd>estimated common factors</dd>
<dt>robust</dt>
<dd>TRUE or FALSE: defines using the classical or robust algorithm to estimate beta</dd>
<dt>method_estimate_beta</dt>
<dd>defines how beta is estimated. Default case is an estimated beta for each individual. Default value is "individual." Possible values are "homogeneous", "group" or "individual".</dd>
<dt>method_estimate_factors</dt>
<dd>specifies the robust algorithm to estimate factors: default is "macro". The value is not used when robust is set to FALSE.</dd>
<dt>verbose</dt>
<dd>when TRUE, it prints messages</dd></dl>

Arguments

Defines the object that will be used to define a initial clustering. — define_object_for_initial_clustering_macropca

<dl>

<dt>Y</dt>
<dd>Y: NxT dataframe with the panel data of interest</dd>


<dt>k</dt>
<dd>number of common factors to be estimated</dd>


<dt>kg</dt>
<dd>number of group specific factors to be estimated</dd>


<dt>comfactor</dt>
<dd>estimated common factors</dd>


<dt>robust</dt>
<dd>TRUE or FALSE: defines using the classical or robust algorithm to estimate beta</dd>


<dt>method_estimate_beta</dt>
<dd>defines how beta is estimated. Default case is an estimated beta for each individual. Default value is "individual." Possible values are "homogeneous", "group" or "individual".</dd>


<dt>method_estimate_factors</dt>
<dd>specifies the robust algorithm to estimate factors: default is "macro". The value is not used when robust is set to FALSE.</dd>


<dt>verbose</dt>
<dd>when TRUE, it prints messages</dd>

</dl>

define_object_for_initial_clustering_macropca: Defines the object that will be used to define a initial clustering.

Description

Usage

Value

Arguments