residual_analysis

<p>Functions for analyzing residual structure, modeling conditional
variance, constructing stochastic differential equations (SDEs), and
iterative GLS estimation for heteroscedastic systems.</p>

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

residual_analysis: Residual Analysis and Stochastic Differential Equations

Description

Arguments