pmm2_variance_factor

Calculate theoretical skewness, kurtosis coefficients and variance reduction factor

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_factor function

<dl><dt>m2, m3, m4</dt>
<dd>central moments of second, third and fourth orders</dd></dl>

Arguments

Calculate theoretical skewness, kurtosis coefficients and variance reduction factor — pmm2_variance_factor

<dl>

<dt>m2, m3, m4</dt>
<dd>central moments of second, third and fourth orders</dd>

</dl>

pmm2_variance_factor: Calculate theoretical skewness, kurtosis coefficients and variance reduction factor

Description

Usage

Value

Arguments