gmwm_param_bootstrapper

Using the bootstrap approach, we simulate a model based on user supplied parameters, obtain the wavelet variance, and then V.

internal

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

St<c3><a9>phane Guerrier

simts

Time Series Analysis Tools

Stéphane Guerrier

James Balamuta

Roberto Molinari

Justin Lee

Lionel Voirol

Yuming Zhang

Wenchao Yang

Nathanael Claussen

Yunxiang Zhang

Christian Gunning

Romain Francois

Ross Ihaka

R Core Team 

gmwm_param_bootstrapper function

<dl><dt>theta</dt>
<dd>A <code>vector</code> with dimensions N x 1 that contains user-supplied initial values for parameters</dd>
<dt>desc</dt>
<dd>A <code>vector&lt;string&gt;</code> indicating the models that should be considered.</dd>
<dt>objdesc</dt>
<dd>A <code>field&lt;vec&gt;</code> that contains an object description (e.g. values) of the model.</dd></dl>

Arguments

Author

Bootstrap for Estimating Both Theta and Theta SD — gmwm_param_bootstrapper

<dl>

<dt>theta</dt>
<dd>A <code>vector</code> with dimensions N x 1 that contains user-supplied initial values for parameters</dd>


<dt>desc</dt>
<dd>A <code>vector&lt;string&gt;</code> indicating the models that should be considered.</dd>


<dt>objdesc</dt>
<dd>A <code>field&lt;vec&gt;</code> that contains an object description (e.g. values) of the model.</dd>

</dl>

Bootstrap for Estimating Both Theta and Theta SD

gmwm_param_bootstrapper: Bootstrap for Estimating Both Theta and Theta SD

Description

Usage

Value

Arguments

Author

Details