Warsaw Econometric Challenge 2024 Winner

The Warsaw Econometric Challenge is Poland's premier academic competition for econometric modeling, attracting top students and researchers from universities across the country. The 2024 edition focused on macroeconomic forecasting using complex, multi-dimensional datasets.

Participants were tasked with developing models to predict key economic indicators including inflation rates, GDP growth, unemployment levels, and currency exchange rates using historical data spanning multiple decades and economic cycles.

My winning approach combined cutting-edge econometric theory with modern machine learning techniques:

• VAR Models: Implemented sophisticated Vector Autoregression models to capture dynamic relationships between economic variables
• Cointegration Analysis: Applied Johansen's method to identify long-run equilibrium relationships between non-stationary time series
• Machine Learning Ensemble: Combined traditional econometric models with Random Forest and Gradient Boosting algorithms
• Regime-Switching Models: Incorporated Markov-switching models to account for structural breaks in economic data

The winning solution introduced several innovative elements:

• Hybrid Model Architecture: Developed a novel framework combining economic theory-driven models with data-driven machine learning approaches
• Dynamic Feature Selection: Implemented adaptive algorithms that adjusted variable selection based on economic regime detection
• Cross-Validation Framework: Designed time-series aware validation procedures respecting temporal structure of economic data
• Uncertainty Quantification: Integrated Bayesian methods to provide confidence intervals and forecast distributions

The model achieved exceptional forecasting accuracy across all evaluated metrics:

• Superior Accuracy: Outperformed all competing models with lowest RMSE and MAE scores
• Robust Performance: Maintained consistent accuracy across different economic periods and market conditions
• Interpretability: Provided clear economic insights and explanation for forecasting decisions
• Statistical Significance: Achieved statistically significant improvements over benchmark models used by central banks

The winning solution received acclaim from the academic panel:

• Methodological Innovation: Recognized for novel combination of traditional econometrics with modern ML techniques
• Theoretical Contribution: Judges praised the rigorous theoretical foundation and practical applicability
• Research Potential: Recommendations for academic publication and further research development
• Industry Relevance: Solution applicable to real-world economic forecasting in financial institutions