sim.restricted

Simulate the common component following an unrestricted factor model that admits a restricted representation;
see the model (C2) in the reference.

Implements methods for network estimation and forecasting of high-dimensional time series
exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model.
See Barigozzi, Cho and Owens (2024+) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and
Owens, Cho and Barigozzi (2024+) <arXiv:2301.11675> accompanying the R package.

Haeran Cho

fnets

Factor-Adjusted Network Estimation and Forecasting for
High-Dimensional Time Series

Matteo Barigozzi

Dom Owens

sim.restricted function

<dl><dt>n</dt>
<dd>sample size</dd>
<dt>p</dt>
<dd>dimension</dd>
<dt>q</dt>
<dd>number of unrestricted factors; number of restricted factors is given by <code>2 * q</code></dd>
<dt>heavy</dt>
<dd>if <code>heavy = FALSE</code>, common shocks are generated from <code>rnorm</code> whereas if <code>heavy = TRUE</code>, from <code>rt</code> with <code>df = 5</code> and then scaled by <code>sqrt(3 / 5)</code></dd></dl>

Arguments

Simulate data from a restricted factor model — sim.restricted

<dl>

<dt>n</dt>
<dd>sample size</dd>


<dt>p</dt>
<dd>dimension</dd>


<dt>q</dt>
<dd>number of unrestricted factors; number of restricted factors is given by <code>2 * q</code></dd>


<dt>heavy</dt>
<dd>if <code>heavy = FALSE</code>, common shocks are generated from <code>rnorm</code> whereas if <code>heavy = TRUE</code>, from <code>rt</code> with <code>df = 5</code> and then scaled by <code>sqrt(3 / 5)</code></dd>

</dl>

sim.restricted: Simulate data from a restricted factor model

Description

Usage

Value

Arguments

References

Examples