get_mu_yt_Cpp

Calculates the conditional means \(\mu_{y,t}\) of the process

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_mu_yt_Cpp function

<dl><dt>obs</dt>
<dd>a \((T \times dp)\) matrix such that the i:th row contains the vector
\((y_{i-1},...,y_{i-p})\) \(((dp)x1)\), where \(y_{i}=(y_{1i},...,y_{di})\)
\((dx1)\). That is, the initial values are included but the last observations not.</dd>
<dt>all_phi0</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</dt>
<dd>a \((d \times dp \times M)\) array such that the slice <code>[, , m]</code>
contains the AR matrices of the m:th regime cbinded together: \([A_{m,1}:...:A_{m,p}]\).</dd>
<dt>alpha_mt</dt>
<dd>a \((T \times M)\) matrix such that <code>[t, m]</code> contains the time t
transition weights of the m:th regime.</dd></dl>

Arguments

Calculate the conditional means of the process — get_mu_yt_Cpp

<dl>

<dt>obs</dt>
<dd>a \((T \times dp)\) matrix such that the i:th row contains the vector
\((y_{i-1},...,y_{i-p})\) \(((dp)x1)\), where \(y_{i}=(y_{1i},...,y_{di})\)
\((dx1)\). That is, the initial values are included but the last observations not.</dd>


<dt>all_phi0</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</dt>
<dd>a \((d \times dp \times M)\) array such that the slice <code>[, , m]</code>
contains the AR matrices of the m:th regime cbinded together: \([A_{m,1}:...:A_{m,p}]\).</dd>


<dt>alpha_mt</dt>
<dd>a \((T \times M)\) matrix such that <code>[t, m]</code> contains the time t
transition weights of the m:th regime.</dd>

</dl>

get_mu_yt_Cpp: Calculate the conditional means of the process

Description

Usage

Value

Arguments