calculate_W

Calculates W = Y - X*beta_est. It is used in the initialization step of the algorithm, to initialise the factorstructures.

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

calculate_W function

<dl><dt>Y</dt>
<dd>Y: NxT dataframe with the panel data of interest</dd>
<dt>X</dt>
<dd>X: NxTxp array containing the observable variables</dd>
<dt>beta_est</dt>
<dd>estimated values of beta</dd>
<dt>g</dt>
<dd>Vector with group membership for all individuals</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>
<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></dl>

Arguments

Calculates W = Y - X*beta_est. It is used in the initialization step of the algorithm, to initialise the factorstructures. — calculate_W

<dl>

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


<dt>X</dt>
<dd>X: NxTxp array containing the observable variables</dd>


<dt>beta_est</dt>
<dd>estimated values of beta</dd>


<dt>g</dt>
<dd>Vector with group membership for all individuals</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>


<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>

</dl>

calculate_W: Calculates W = Y - X*beta_est. It is used in the initialization step of the algorithm, to initialise the factorstructures.

Description

Usage

Value

Arguments