skewed_t_dens

<code>skewed_t_dens</code> calculates the density of the univariate skewed t distribution described in Hansen (1994).

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

skewed_t_dens function

<dl><dt>y</dt>
<dd>a numeric vector containing the values at which the density is to be evaluated.</dd>
<dt>nu</dt>
<dd>the degrees of freedom parameter value, a numeric scalar strictly larger than two.</dd>
<dt>lambda</dt>
<dd>the skewness parameter value, a numeric scalar strictly between -1 and 1.</dd></dl>

Arguments

The density function of the univariate skewed t distribution — skewed_t_dens

<dl>

<dt>y</dt>
<dd>a numeric vector containing the values at which the density is to be evaluated.</dd>


<dt>nu</dt>
<dd>the degrees of freedom parameter value, a numeric scalar strictly larger than two.</dd>


<dt>lambda</dt>
<dd>the skewness parameter value, a numeric scalar strictly between -1 and 1.</dd>

</dl>

skewed_t_dens: The density function of the univariate skewed t distribution

Description

Usage

Value

Arguments

Details

References