compare_estimation_methods

Produces a comparison of drift estimates from ordinary least squares
versus iterative GLS.

A comprehensive toolkit for discovering differential and difference
equations from empirical time series data using symbolic regression. The package
implements a complete workflow from data preprocessing (including Total Variation
Regularized differentiation for noisy economic data), visual exploration of
dynamical structure, and symbolic equation discovery via genetic algorithms.
It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide
industrial-grade robustness, physics-informed constraints, and rigorous
out-of-sample validation. Designed for economists, physicists, and researchers
studying dynamical systems from observational data.

José Mauricio Gómez Julián

EmpiricalDynamics

Empirical Discovery of Differential Equations from Time Series
Data

compare_estimation_methods function

<dl><dt>ols_model</dt>
<dd>SDE model estimated with OLS</dd>
<dt>gls_model</dt>
<dd>SDE model estimated with iterative GLS</dd>
<dt>data</dt>
<dd>Data frame used for estimation</dd></dl>

Arguments

Compare OLS and GLS Estimation — compare_estimation_methods

<dl>

<dt>ols_model</dt>
<dd>SDE model estimated with OLS</dd>


<dt>gls_model</dt>
<dd>SDE model estimated with iterative GLS</dd>


<dt>data</dt>
<dd>Data frame used for estimation</dd>

</dl>

compare_estimation_methods: Compare OLS and GLS Estimation

Description

Usage

Value

Arguments