print.hypotest

<code>print.hypotest</code> is the print method for the class hypotest
 objects.

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

print.hypotest function

<dl><dt>x</dt>
<dd>object of class <code>'hypotest'</code> generated by the function <code>Wald_test</code>, <code>LR_test</code>,
<code>Rao_test</code>, or <code>Portmenteau_test</code>.</dd>
<dt>...</dt>
<dd>currently not in use.</dd>
<dt>digits</dt>
<dd>how many significant digits to print?</dd></dl>

Arguments

Print method for the class hypotest — print.hypotest

<dl>

<dt>x</dt>
<dd>object of class <code>'hypotest'</code> generated by the function <code>Wald_test</code>, <code>LR_test</code>,
<code>Rao_test</code>, or <code>Portmenteau_test</code>.</dd>


<dt>...</dt>
<dd>currently not in use.</dd>


<dt>digits</dt>
<dd>how many significant digits to print?</dd>

</dl>

print.hypotest: Print method for the class hypotest

Description

Usage

Value

Arguments