get_new_start

<code>get_new_start</code> calculates the new starting time of series
 that is forwarded some number of steps.

internal

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 (2024) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

get_new_start function

<dl><dt>y_start</dt>
<dd>original starting time of the series</dd>
<dt>y_freq</dt>
<dd>frequency of the series</dd>
<dt>steps_forward</dt>
<dd>how many steps the series should be forwarded?</dd></dl>

Arguments

Get the new starting time of series that is forwarded some number of steps — get_new_start

<dl>

<dt>y_start</dt>
<dd>original starting time of the series</dd>


<dt>y_freq</dt>
<dd>frequency of the series</dd>


<dt>steps_forward</dt>
<dd>how many steps the series should be forwarded?</dd>

</dl>

get_new_start: Get the new starting time of series that is forwarded some number of steps

Description

Usage

Value

Arguments