compare_ar_methods

Implements the Polynomial Maximization Method ('PMM') for parameter estimation
in linear and time series models when error distributions deviate from normality.
The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary
least squares ('OLS') when errors exhibit significant skewness. Includes methods
for linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference.
Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>,
Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and
Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.

Serhii Zabolotnii

EstemPMM

Polynomial Maximization Method for Non-Gaussian Regression

compare_ar_methods function

<dl><dt>x</dt>
<dd>Numeric vector of time series data</dd>
<dt>order</dt>
<dd>Model order specification (see ts_pmm2 for format)</dd>
<dt>include.mean</dt>
<dd>Logical, whether to include intercept term</dd>
<dt>pmm2_args</dt>
<dd>List of additional arguments to pass to ts_pmm2()</dd></dl>

Arguments

Compare AR methods — compare_ar_methods

<dl>

<dt>x</dt>
<dd>Numeric vector of time series data</dd>


<dt>order</dt>
<dd>Model order specification (see ts_pmm2 for format)</dd>


<dt>include.mean</dt>
<dd>Logical, whether to include intercept term</dd>


<dt>pmm2_args</dt>
<dd>List of additional arguments to pass to ts_pmm2()</dd>

</dl>

compare_ar_methods: Compare AR methods

Description

Usage

Value

Arguments