structural-change

The ultimate Python package for structural change in time-series econometrics and forecasting

Rational expectations solutions under structural change (Hatcher 2022, JEDC)

Bai–Perron structural break detection and estimation for time series and panel data. Tests for breaks, estimates break dates with confidence intervals, and selects break counts via sequential testing or information criteria.

A general equilibrium, multi-sector, -gender, and -production technology model for schooling choices

Repository containing the main results of the LinsPlit technical report.

This repository presents a minimal early‑warning method for failure detection. Before structural failure, the number of critical points in the signal increases. By extracting raw, smoothed, and critical‑point sequences, and tracking the critical‑point time series, structural changes can be quantified as an early signal.

Code, frozen protocols, and derived evidence for Learnable Is Not Transportable

Data-driven insights on the Non-Alcoholic Beverage market using macroeconomic trends, consumer behavior, and forecasting models. Covers price elasticity, consumption patterns, and strategic recommendations for industry growth.