get_y_t

<code>get_y_t</code> computes the observation \(y_t=\mu_{y,t} + B_{y,t}e_t\) for a single time period
 based on the conditional mean, impact matrix, and shock vector.

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

get_y_t function

<dl><dt>mu_yt</dt>
<dd>a \((d \times 1)\) vector of the conditional mean for the time period \(t\).</dd>
<dt>B_yt</dt>
<dd>a \((d \times d)\) impact matrix for the time period \(t\).</dd>
<dt>e_t</dt>
<dd>a \((d \times 1)\) vector of the structural shocks for the time period \(t\).</dd></dl>

Arguments

Compute the observation \(y_t=\mu_{y,t} + B_{y,t}e_t\) for a single time period — get_y_t

<dl>

<dt>mu_yt</dt>
<dd>a \((d \times 1)\) vector of the conditional mean for the time period \(t\).</dd>


<dt>B_yt</dt>
<dd>a \((d \times d)\) impact matrix for the time period \(t\).</dd>


<dt>e_t</dt>
<dd>a \((d \times 1)\) vector of the structural shocks for the time period \(t\).</dd>

</dl>

get_y_t: Compute the observation \(y_t=\mu_{y,t} + B_{y,t}e_t\) for a single time period

Description

Usage

Value

Arguments

Details