create_ts_design_matrix

Create design matrix for time series

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

create_ts_design_matrix function

<dl><dt>x</dt>
<dd>Time series data</dd>
<dt>model_info</dt>
<dd>List with model parameters</dd>
<dt>innovations</dt>
<dd>Optional innovations/residuals for MA components</dd></dl>

Arguments

Create design matrix for time series — create_ts_design_matrix

<dl>

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


<dt>model_info</dt>
<dd>List with model parameters</dd>


<dt>innovations</dt>
<dd>Optional innovations/residuals for MA components</dd>

</dl>

create_ts_design_matrix: Create design matrix for time series

Description

Usage

Value

Arguments