My research combines economics, econometrics, and machine learning to understand social phenomena, anticipate crises, and inform policy decisions. Recently, I have developed data-driven forecasting tools and applied causal methods in panel data to study economic dynamics in real-world settings.
Earlier, I worked as a macroeconomic specialist in Peru 🇵🇪, focusing on macro modeling, forecasting, and scenario analysis. I continue to collaborate on applied projects, which keeps my work grounded in practical policy questions.
I care about transparent and reproducible research and enjoy sharing tools and code whenever possible.
📌 Recent Updates
- 📄 New working paper: Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events, with L. Mayoral, H. Mueller, C. Rauh, and M. Phillip. Working Paper.
- 🧠 Released a pre-publication version of MacroPy, a toolbox for macroeconometric analysis in Python. Install via .whl here.
- ✍️ New blog post: Bayesian VARs in Python. A step-by-step tutorial on Bayesian VARs, from scratch and with
MacroPy.