pmm2_variance_matrices

Calculate theoretical variance matrices for OLS and PMM2

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

pmm2_variance_matrices function

<dl><dt>X</dt>
<dd>Design matrix with column of ones</dd>
<dt>m2, m3, m4</dt>
<dd>central moments of OLS residuals</dd></dl>

Arguments

Calculate theoretical variance matrices for OLS and PMM2 — pmm2_variance_matrices

<dl>

<dt>X</dt>
<dd>Design matrix with column of ones</dd>


<dt>m2, m3, m4</dt>
<dd>central moments of OLS residuals</dd>

</dl>

pmm2_variance_matrices: Calculate theoretical variance matrices for OLS and PMM2

Description

Usage

Value

Arguments