create_covMat_crosssectional_dependence

Used to include cross-sectional dependence or serial dependence into the simulated panel data.

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

create_covMat_crosssectional_dependence function

<dl><dt>parameter</dt>
<dd>amount of cross-sectional dependence</dd>
<dt>NN</dt>
<dd>number of time series</dd></dl>

Arguments

Function used in generating simulated data with non normal errors. — create_covMat_crosssectional_dependence

<dl>

<dt>parameter</dt>
<dd>amount of cross-sectional dependence</dd>


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

</dl>

create_covMat_crosssectional_dependence: Function used in generating simulated data with non normal errors.

Description

Usage

Value

Arguments