compute_ts_residuals

Compute final residuals for time series models

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

compute_ts_residuals function

<dl><dt>coefs</dt>
<dd>Estimated coefficients</dd>
<dt>model_info</dt>
<dd>Model information</dd></dl>

Arguments

Compute final residuals for time series models — compute_ts_residuals

<dl>

<dt>coefs</dt>
<dd>Estimated coefficients</dd>


<dt>model_info</dt>
<dd>Model information</dd>

</dl>

compute_ts_residuals: Compute final residuals for time series models

Description

Usage

Value

Arguments