Assistant Professor of Economics | Forecasting β’ Structural Modelling β’ Macroeconomics
I develop and maintain high-frequency, real-time datasets designed to eliminate look-ahead bias and improve forecasting accuracy. My goal is to bridge the gap between academic theory and practical application through open-source code and reproducible data.
High-frequency and real-time datasets for Econometrics and Machine Learning.
Key Advantage: End-of-month (EOM) and real-time vintage data eliminate temporal aggregation bias and look-ahead bias. Switching from monthly averages to EOM data can improve forecasting accuracy by up to 40%.
| Dataset | Scope | Tech Highlights |
|---|---|---|
| Real-Time Daily & EOM EERs | 160 Countries | World's first real-time daily/EOM effective exchange rate dataset. |
| 17 Primary Commodities | Global Markets | Mixed-frequency spot data & futures-based forecasts. |
| Real-Time Oil Vintages | Monthly Vintages | Real-time global crude oil production, activity, and inventories. |
| EOM Backcasted Oil Prices | Since 1973 | WTI/Brent EOM spot prices. Optimized for Random Walk testing. |
Level up your data analysis from "Zero to Hero."
- πΊ The Stata Economics Masterclass (Complete: 5 Videos + Code)
- π» The R Economics Masterclass (In Development: Starter code available now, videos coming soon!)
- Automated Import & Cleaning: Stop wasting time on manual data formatting.
- Debug Like a Pro: Identifying AI "slop" and structural errors.
- Core Skills: Graphing, priors, and predictive modeling.
- Monte Carlo: Using simulations to verify your results.
- The "Copy-Paste" Intervention: Full automation of result reporting.
- π¨π¦ Canadian Economic Data Guide: A streamlined gateway to Canadian Data.