get_hetsked_sstvar

<code>get_hetsked_sstvar</code> constructs structural STVAR model identified by heteroskedasticity
 based on a reduced form STVAR model.

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_hetsked_sstvar function

<dl><dt>stvar</dt>
<dd>a an object of class <code>'stvar'</code>, created by, e.g., <code>fitSTVAR</code>,
specifying a reduced form or a structural model</dd>
<dt>calc_std_errors</dt>
<dd>Calculate approximate standard errors (based on standard asymptotics)?</dd></dl>

Arguments

Switch from two-regime reduced form STVAR model to a structural model identified by heteroskedasticity — get_hetsked_sstvar

<dl>

<dt>stvar</dt>
<dd>a an object of class <code>'stvar'</code>, created by, e.g., <code>fitSTVAR</code>,
specifying a reduced form or a structural model</dd>


<dt>calc_std_errors</dt>
<dd>Calculate approximate standard errors (based on standard asymptotics)?</dd>

</dl>

get_hetsked_sstvar: Switch from two-regime reduced form STVAR model to a structural model identified by heteroskedasticity

Description

Usage

Value

Arguments

Details

See Also