far_1_S

`far_1_S` Simulates an FAR(1,S)-fGARCH(1,1) process with N independent observations, each
 observed discretely at J points on the interval [0,1].

Provides a collection of white noise hypothesis tests for functional time series and related visualizations.
These include tests based on the norms of autocovariance operators that are built under both strong and weak
white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
dimensional reduction are included, which are built under strong white noise assumptions.
Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019)
<doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>,
and Kim et al. (2023) <doi: 10.1214/23-SS143>
respectively.

Mihyun Kim

wwntests

Hypothesis Tests for Functional Time Series

Daniel Petoukhov

far_1_S function

<dl><dt>N</dt>
<dd>the number of fGARCH(1,1) curves to sample.</dd>
<dt>J</dt>
<dd>the number of points at which each curve is sampled (the resolution of the data).</dd>
<dt>S</dt>
<dd>the autoregressive operator of the model, between 0 and 1, indicating the level of
conditional heteroscedasticity.</dd>
<dt>type</dt>
<dd>the assumed model of the error term. The default argument is 'IID', under which
the errors are assumed to be independent and identically distributed. The alternative argument
is 'fGARCH', which will assume that the errors follow an fGARCH(1,1) process.</dd>
<dt>burn_in</dt>
<dd>the number of initial samples to burn (discard).</dd></dl>

Arguments

`far_1_S` Simulates an FAR(1,S)-fGARCH(1,1) process with N independent observations, each
 observed discretely at J points on the interval [0,1]. — far_1_S

<dl>

<dt>N</dt>
<dd>the number of fGARCH(1,1) curves to sample.</dd>


<dt>J</dt>
<dd>the number of points at which each curve is sampled (the resolution of the data).</dd>


<dt>S</dt>
<dd>the autoregressive operator of the model, between 0 and 1, indicating the level of
conditional heteroscedasticity.</dd>


<dt>type</dt>
<dd>the assumed model of the error term. The default argument is 'IID', under which
the errors are assumed to be independent and identically distributed. The alternative argument
is 'fGARCH', which will assume that the errors follow an fGARCH(1,1) process.</dd>


<dt>burn_in</dt>
<dd>the number of initial samples to burn (discard).</dd>

</dl>

far_1_S: `far_1_S` Simulates an FAR(1,S)-fGARCH(1,1) process with N independent observations, each observed discretely at J points on the interval [0,1].

Description

Usage

Value

Arguments

Examples