BrianGithinji-BMG/repo-rate-forecaster

Hybrid ARDL × XGBoost web app forecasting Kenya's monthly Central Bank Repo Rate. Built on the BFE 4.2 dissertation. # Repo Rate Forecaster — Hybrid ARDL × XGBoost An interactive web app that forecasts Kenya's monthly Central Bank Repo Rate using the hybrid **ARDL + XGBoost** framework from the BFE 4.2 dissertation *"Predicting Repurchase Rates Using a Hybrid ARDL–XGBoost Model"*. ## Features - **Three models in one UI** — pick ARDL, XGBoost, or the Hybrid combination - **Live forecasts** with confidence bands and per-month contribution breakdown - **Quick presets** — late-2024 baseline, easing scenario, inflation-shock scenario - **Editable lag history** so you can stress-test the model against custom paths - **Warm light theme**, Framer Motion micro-interactions, Recharts visualisations ## Tech - **Next.js 16** App Router + TypeScript + Tailwind 4 - **Framer Motion** for animation - **Recharts** for the forecast chart - **Vercel Python serverless** for XGBoost & Hybrid (uses `xgboost`, `pandas`, `numpy`) - ARDL coefficients are hardcoded from the fitted model — pure ARDL forecasts run client-side with zero dependencies ## Local development ```bash npm install npm run dev ``` For XGBoost / Hybrid forecasts you'll be asked to upload a CSV/XLSX with these columns: ``` date, repo, inflation, usd_ksh, m2 ``` This is the same shape as `Combined_data.xlsx` from the original notebook. ## Deploy ```bash vercel --prod ``` The Python serverless function lives in `api/forecast.py` with its own `requirements.txt`.