GNARsim

Simulates a GNAR process with Normally distributed innovations.

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.

Matt Nunes

GNAR

Methods for Fitting Network Time Series Models

Kathryn Leeming

Guy Nason

Marina Knight

James Wei

Daniel Salnikov

Mario Cortina Borja

GNARsim function

<dl><dt>n</dt>
<dd>time length of simulation.</dd><dt>net</dt>
<dd>network used for the GNAR simulation.</dd><dt>alphaParams</dt>
<dd>a list containing vectors of auto-regression parameters for each time-lag.</dd><dt>betaParams</dt>
<dd>a list of equal length as <code>alphaParams</code> containing the network-regression parameters for each time-lag.</dd><dt>sigma</dt>
<dd>the standard deviation for the innovations.</dd><dt>tvnets</dt>
<dd>Only NULL is currently supported.</dd><dt>netsstart</dt>
<dd>Only NULL is currently supported.</dd></dl>

Arguments

Simulates a GNAR process — GNARsim

<dl>

<dt>n</dt>
<dd>time length of simulation.</dd>

<dt>net</dt>
<dd>network used for the GNAR simulation.</dd>

<dt>alphaParams</dt>
<dd>a list containing vectors of auto-regression parameters for each time-lag.</dd>

<dt>betaParams</dt>
<dd>a list of equal length as <code>alphaParams</code> containing the network-regression parameters for each time-lag.</dd>

<dt>sigma</dt>
<dd>the standard deviation for the innovations.</dd>

<dt>tvnets</dt>
<dd>Only NULL is currently supported.</dd>

<dt>netsstart</dt>
<dd>Only NULL is currently supported.</dd>

</dl>

GNARsim: Simulates a GNAR process

Description

Usage

Value

Arguments

Details

References

Examples