Mexico's Economic Crossroads (2026-2027)

A quantitative analysis of Mexico's fiscal outlook using a KLR Signals-based Monte Carlo simulation with 100,000 iterations.

Overview

This repository contains the complete Python implementation powering the analysis for the Medium article "Mexico's Economic Crossroads: A Data-Driven Analysis of the 2026-2027 Fiscal Landscape" by Dr. Robert Hernández Martínez. Article on Medium: https://medium.com/@chomchom216/mexicos-economic-crossroads-41aaedeff8b5

Key Features

• Composite Crisis Probability Model (CCPM): KLR signals-based Monte Carlo simulation
• 8 Key Indicators: GDP Growth, Core Inflation, Sovereign Rating, Banking Sector, Fiscal Deficit, Debt Service, USMCA Uncertainty, Institutional Quality
• Real Estate Trajectory Simulation: Predictive modeling of residential property value trends
• Interactive Visualizations: All charts from the article generated directly from the code

Methodology

The model integrates three established academic frameworks:

1. Kaminsky-Lizondo-Reinhart (KLR) Signals Approach (1998)
2. Berg-Pattillo Probit/Logit Framework (1999)
3. World Bank Panel Logit Methodology (2021)

Results

• Point Estimate: 74.1% crisis probability
• 80% Confidence Interval: [54.91%, 85.65%]
• P(Crisis > 50%): 94.56%
• Peak Risk Period: February 2027

Requirements

• Python 3.7+
• Jupyter Notebook
• Required packages: numpy, pandas, matplotlib, seaborn, tqdm

Run the Analysis

pip install -r requirements.txt
jupyter notebook "Mexico's_Economic_Crossroads_A_Data_Driven_Analysis_of_the_2026_2027_Fiscal_Landscape_Dr_Robert_Hernández_Martínez.ipynb"