Predicting Change in GDP of the United States
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Updated
May 10, 2020 - R
Predicting Change in GDP of the United States
Forecasting GDP using MIDAS regressions with mixed-frequency macroeconomic indicators; includes data preparation, model estimation, and evaluation.
End-to-end predictive and econometric modeling with R, covering regression, classification, churn prediction, and GDP forecasting. Demonstrates expertise in EDA, feature engineering, model building, and statistical evaluation.
World Bank GDP analytics dashboard — multi-country line comparison, PPP choropleth map, per-capita trends, income group aggregation, CAGR computation, and ARIMA projection.
Streamlit GDP dashboard powered by World Bank open data — country economic output visualisation, growth rate bar charts, regional aggregation, and CSV export.
End-to-End Python implementation of Shin (2026)'s evaluator-locked agentic loop for transparent empirical research. Combines LLM-driven specification search with immutable evaluation harnesses, penalized regression (peLASSO), and Diebold-Mariano testing on ECB forecast data. Addresses the "garden of forking paths" crisis in AI-driven economics.
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