This auxiliary function defines the regime structure of a multivariate TAR model by
specifying the number of regimes and the corresponding lag orders for the endogenous,
exogenous, and threshold series in each regime.

Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.

Luis Hernando Vanegas

mtarm

Bayesian Estimation of Multivariate Threshold Autoregressive
Models

Sergio Alejandro Calderón

Luz Marina Rondón

ars function

<dl><dt>nregim</dt>
<dd>A positive integer indicating the total number of regimes.</dd>
<dt>p</dt>
<dd>A list of positive integers specifying the autoregressive order of the output series
within each regime.</dd>
<dt>q</dt>
<dd>A list of non-negative integers specifying the maximum lag of the exogenous series
within each regime.</dd>
<dt>d</dt>
<dd>A list of non-negative integers specifying the maximum lag of the threshold series
within each regime.</dd></dl>

Arguments

Auxiliary function to specify the number of regimes and lag orders — ars

<dl>

<dt>nregim</dt>
<dd>A positive integer indicating the total number of regimes.</dd>


<dt>p</dt>
<dd>A list of positive integers specifying the autoregressive order of the output series
within each regime.</dd>


<dt>q</dt>
<dd>A list of non-negative integers specifying the maximum lag of the exogenous series
within each regime.</dd>


<dt>d</dt>
<dd>A list of non-negative integers specifying the maximum lag of the threshold series
within each regime.</dd>

</dl>

ars: Auxiliary function to specify the number of regimes and lag orders

Description

Usage

Value

Arguments