gen_coef_mat

Simulates coefficient matrices used to generate data from a vector autoregressive process.

Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) <doi:10.48550/arXiv.2511.19761>.

Michael Hellstern

micvar

Order Selection in Vector Autoregression by Mean Square
Information Criteria

gen_coef_mat function

<dl><dt>k</dt>
<dd>Integer. Dimension of process.</dd>
<dt>coefmin</dt>
<dd>Numeric. Minimum value of coefficient. See Details.</dd>
<dt>coefmax</dt>
<dd>Numeric. Maximum value of coefficient. See Details.</dd>
<dt>dens</dt>
<dd>Numeric. Must be between 0 and 1. Specifies the proportion of non-zero entries in the coefficient matrix. The number of non-zero entries is computed as <code>floor(k^2*dens)</code>.</dd></dl>

Arguments

Simulate coefficient matrices with specified density — gen_coef_mat

<dl>

<dt>k</dt>
<dd>Integer. Dimension of process.</dd>


<dt>coefmin</dt>
<dd>Numeric. Minimum value of coefficient. See Details.</dd>


<dt>coefmax</dt>
<dd>Numeric. Maximum value of coefficient. See Details.</dd>


<dt>dens</dt>
<dd>Numeric. Must be between 0 and 1. Specifies the proportion of non-zero entries in the coefficient matrix. The number of non-zero entries is computed as <code>floor(k^2*dens)</code>.</dd>

</dl>

gen_coef_mat: Simulate coefficient matrices with specified density

Description

Usage

Value

Arguments

Details

Examples