get_Bt_Cpp

This internal function takes a cube of matrices (<code>all_Omegas</code>) and a matrix of weights (<code>alpha_mt</code>),
and calculates the weighted sums of the matrices in the cube. For each row in <code>alpha_mt</code>, it computes
a weighted sum of matrices, and returns the

internal

Penalized and non-penalized maximum likelihood estimation of smooth
transition vector autoregressive models with various types of transition weight
functions, conditional distributions, and identification methods. Constrained
estimation with various types of constraints is available. Residual based
model diagnostics, forecasting, simulations, and calculation of impulse
response functions, generalized impulse response functions, and generalized
forecast error variance decompositions. See
Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

get_Bt_Cpp function

<dl><dt>all_Omegas</dt>
<dd>A cube (3D array) of impact matrices, with each slice being an inveritble square matrix.</dd>
<dt>alpha_mt</dt>
<dd>A matrix of weights, with as many columns as there are slices in <code>all_Omegas</code>.</dd></dl>

Arguments

Calculate the impact matrix \(B_t\) for all \(t\) for models with a non-Gaussian
 conditional distribution with mutually independent shocks. — get_Bt_Cpp

<dl>

<dt>all_Omegas</dt>
<dd>A cube (3D array) of impact matrices, with each slice being an inveritble square matrix.</dd>


<dt>alpha_mt</dt>
<dd>A matrix of weights, with as many columns as there are slices in <code>all_Omegas</code>.</dd>

</dl>

get_Bt_Cpp: Calculate the impact matrix \(B_t\) for all \(t\) for models with a non-Gaussian conditional distribution with mutually independent shocks.

Description

Usage

Value

Arguments