update_ma_innovations

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

update_ma_innovations function

<dl><dt>x</dt>
<dd>Centered time series</dd>
<dt>ma_coef</dt>
<dd>Vector of MA coefficients</dd></dl>

Arguments

Update MA model innovations — update_ma_innovations

<dl>

<dt>x</dt>
<dd>Centered time series</dd>


<dt>ma_coef</dt>
<dd>Vector of MA coefficients</dd>

</dl>

update_ma_innovations: Update MA model innovations

Description

Usage

Value

Arguments