unWvec

<code>unWvec</code> forms a square matrix from a vector of
 stacked columns where zeros are removed according to impact
 matrix constraints.

internal

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 (2024) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

unWvec function

<dl><dt>Wvector</dt>
<dd>a length \(d^2 - n_zeros\) vector where \(n_zeros\) is the
number of zero entries in the impact matrix.</dd>
<dt>d</dt>
<dd>the number of rows in the square matrix to be formed.</dd></dl>

Arguments

Reverse vectorization operator that restores zeros — unWvec

<dl>

<dt>Wvector</dt>
<dd>a length \(d^2 - n_zeros\) vector where \(n_zeros\) is the
number of zero entries in the impact matrix.</dd>


<dt>d</dt>
<dd>the number of rows in the square matrix to be formed.</dd>

</dl>

unWvec: Reverse vectorization operator that restores zeros

Description

Usage

Value

Arguments