Forecasting GDP using MIDAS regressions with mixed-frequency macroeconomic indicators; includes data preparation, model estimation, and evaluation.
End-to-End Python replication of Iadisernia & Camassa’s LLM macroeconomic forecasting methodology (ICAIF 2025). Implements: 2,368 synthetic economist profiles, 120,000+ GPT-4o forecasts across 50 European Central Bank (ECB) SPF rounds, a rigorous ablation study with Monte Carlo & binomial hypothesis testing.
End-to-end Python implementation of Ma et al.'s (2025) matrix-variate diffusion index models for macroeconomic forecasting. Features α-PCA factor extraction, supervised screening, and ILS estimation for high-dimensional forecasting with preserved structural information.
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