calculate_errors_virtual_groups

During the updating of group membership, the errorterm is used as the objective function to estimate the group.

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_errors_virtual_groups function

<dl><dt>group</dt>
<dd>group</dd>
<dt>LF</dt>
<dd>NxT-matrix of the common factorstructure</dd>
<dt>virtual_grouped_factor_structure</dt>
<dd>list with length the number of groups; every element of the list contains NxT-matrix</dd>
<dt>NN</dt>
<dd>number of time series</dd>
<dt>TT</dt>
<dd>length of time series</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>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>
<dt>Y</dt>
<dd>Y: NxT dataframe with the panel data of interest</dd>
<dt>X</dt>
<dd>dataframe with the observed variables</dd>
<dt>beta_est</dt>
<dd>estimated values of beta</dd>
<dt>g</dt>
<dd>Vector with estimated group membership for all individuals</dd></dl>

Arguments

Helpfunction for update_g(). Calculates the errors for one of the possible groups time series can be placed in. — calculate_errors_virtual_groups

<dl>

<dt>group</dt>
<dd>group</dd>


<dt>LF</dt>
<dd>NxT-matrix of the common factorstructure</dd>


<dt>virtual_grouped_factor_structure</dt>
<dd>list with length the number of groups; every element of the list contains NxT-matrix</dd>


<dt>NN</dt>
<dd>number of time series</dd>


<dt>TT</dt>
<dd>length of time series</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>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>


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


<dt>X</dt>
<dd>dataframe with the observed variables</dd>


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


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

</dl>

calculate_errors_virtual_groups: Helpfunction for update_g(). Calculates the errors for one of the possible groups time series can be placed in.

Description

Usage

Value

Arguments