validate_ts_parameters

Validate and prepare time series parameters

internal

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

validate_ts_parameters function

<dl><dt>x</dt>
<dd>Time series data</dd>
<dt>order</dt>
<dd>Model order specification</dd>
<dt>model_type</dt>
<dd>Model type (ar, ma, arma, or arima)</dd>
<dt>include.mean</dt>
<dd>Whether to include mean/intercept</dd></dl>

Arguments

Validate and prepare time series parameters — validate_ts_parameters

<dl>

<dt>x</dt>
<dd>Time series data</dd>


<dt>order</dt>
<dd>Model order specification</dd>


<dt>model_type</dt>
<dd>Model type (ar, ma, arma, or arima)</dd>


<dt>include.mean</dt>
<dd>Whether to include mean/intercept</dd>

</dl>

validate_ts_parameters: Validate and prepare time series parameters

Description

Usage

Value

Arguments