unvec

<code>unvec</code> forms a square matrix from a vector of
 stacked columns, stacked by <code>vec</code>.

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, counterfactual analysis, and
computation of impulse response functions, generalized impulse response functions,
generalized forecast error variance decompositions, as well as historical
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

unvec function

<dl><dt>d</dt>
<dd>the number of rows in the square matrix to be formed.</dd>
<dt>a</dt>
<dd>a size \((d^2x1)\) vector to be unvectorized into a \((dxd)\) matrix.</dd></dl>

Arguments

Reverse vectorization operator — unvec

<dl>

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


<dt>a</dt>
<dd>a size \((d^2x1)\) vector to be unvectorized into a \((dxd)\) matrix.</dd>

</dl>

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unvec: Reverse vectorization operator

Description

Usage

Value

Arguments