Technical Stack
Economic Toolkit
Detailed overview of the tools and libraries I use for econometric modeling, data engineering, and reproducible science.
Programming & Econometrics
Python (Core Stack)
My primary environment. I use Pandas for data wrangling, NumPy for numerical computation, and Matplotlib/Seaborn/Plotly for visualization.
Econometric Libraries
Experience with Statsmodels (regression, time series), Linearmodels (panel data), and Scikit-learn for predictive modeling.
Other Tools
Solid experience with SQL for database extraction, as well as knowledge of R (Tidyverse) and SAS/Stata from academic projects.
Reproducible Analysis
LaTeX & Typography
All my academic reports are written in LaTeX to ensure precise formatting of mathematical expressions and references.
Git & Version Control
Using Git and GitHub to ensure full traceability in code and analysis. Experience with branching strategies and CI/CD.
Notebooks & Documentation
Interactive Jupyter Notebooks for exploratory analysis and Quarto/Markdown for documentation.