gendata

A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.

Jiaqi Hu

REFA

Robust Exponential Factor Analysis

Xueqin Wang

gendata function

<dl><dt>seed</dt>
<dd>The <code>seed</code> used in the data generation process.</dd>
<dt>T</dt>
<dd>Time dimension.</dd>
<dt>N</dt>
<dd>Cross-sectional dimension.</dd>
<dt>type</dt>
<dd>The type of the data generation process. Options include "1a", "1b", "1c", "1d", "2a", "2b", "2c", "2d", "3a", "3b".</dd></dl>

Arguments

Author

Data generation process — gendata

<dl>

<dt>seed</dt>
<dd>The <code>seed</code> used in the data generation process.</dd>


<dt>T</dt>
<dd>Time dimension.</dd>


<dt>N</dt>
<dd>Cross-sectional dimension.</dd>


<dt>type</dt>
<dd>The type of the data generation process. Options include "1a", "1b", "1c", "1d", "2a", "2b", "2c", "2d", "3a", "3b".</dd>

</dl>

gendata: Data generation process

Description

Usage

Value

Arguments

Author

References

Examples