get_mu_yt

<code>get_mu_yt</code> computes the conditional mean \(\mu_{y,t}=\phi_{y,t} + \sum_{i=1}^pA_{y,t,i}y_{t-i}\) for a single time period
 based on the intercepts, AR matrices, and the vector of lagged observations.

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_mu_yt function

<dl><dt>phi_yt</dt>
<dd>a \((d \times M)\) matrix such that the \(m\)th column contains the intercept parameters of the \(m\)th regime.</dd>
<dt>all_A_yti</dt>
<dd>a 3D array containing the coefficient matrices for the given time period so that the lag \(i\) coefficient matrix
\(A_{y,t,i}\) can be obtained by choosing <code>[, , i]</code>.</dd>
<dt>bold_y_t_minus_1</dt>
<dd>a \((dp \times 1)\) vector \(\boldsymbol{y}_{t-1}=(y_{t-1},...,y_{t-p})\) containing the lagged observations
for the time period \(t\).</dd></dl>

Arguments

Compute the conditional mean \(\mu_{y,t}=\phi_{y,t} + \sum_{i=1}^pA_{y,t,i}y_{t-i}\) for a single time period — get_mu_yt

<dl>

<dt>phi_yt</dt>
<dd>a \((d \times M)\) matrix such that the \(m\)th column contains the intercept parameters of the \(m\)th regime.</dd>


<dt>all_A_yti</dt>
<dd>a 3D array containing the coefficient matrices for the given time period so that the lag \(i\) coefficient matrix
\(A_{y,t,i}\) can be obtained by choosing <code>[, , i]</code>.</dd>


<dt>bold_y_t_minus_1</dt>
<dd>a \((dp \times 1)\) vector \(\boldsymbol{y}_{t-1}=(y_{t-1},...,y_{t-p})\) containing the lagged observations
for the time period \(t\).</dd>

</dl>

get_mu_yt: Compute the conditional mean \(\mu_{y,t}=\phi_{y,t} + \sum_{i=1}^pA_{y,t,i}y_{t-i}\) for a single time period

Description

Usage

Value

Arguments

Details