Coding & Computational Projects

← Back to main portfolio page

Repositories with a strong coding and computational focus, ranging from econometric research pipelines to data infrastructure and computational economics coursework. All projects are open-source on GitHub.

Do investment tax credits foster innovation? The AITC and patent applications

A two-way fixed effects difference-in-differences study of the Alberta Investor Tax Credit (AITC) and its effect on patent applications, implemented entirely in R. The repository includes raw data pipelines, cleaning scripts, econometric estimation, and a fully reproducible Quarto manuscript.

Sánchez Pazmiño, D. H. (2024). Do investment tax credits foster innovation? The Alberta Investor Tax Credit and patent applications. MA Economics Capstone Paper, Simon Fraser University.

Retrospectively sweaty? Temperature changes and presidential approval

An R-based research pipeline matching daily CPC Global Unified temperature raster data to AmericasBarometer survey microdata at the respondent level, using geocoded coordinates and date of interview. Includes data cleaning, spatial joins, and panel regression estimation.

Sánchez Pazmiño, D. H. (2024). Retrospectively sweaty? The effects of temperature changes on presidential approval. POL837 Issues in Comparative Politics Research Paper, Simon Fraser University.

ECON832: Computational Methods for Economics

Course repository for ECON832 at Simon Fraser University, covering computational methods used in economic research. Includes lecture notes, problem sets, and code examples implementing numerical and simulation-based techniques in Julia and R.

datos-reess: Ecuador Social Security Employment Statistics

A data infrastructure project producing cleaned, analysis-ready datasets from Ecuador’s Registro Estadístico de Empleo en la Seguridad Social (REESS), the official social security employment registry published by INEC. The dataset spans from January 2009 onward and is too large for in-memory processing, so data ingestion and transformation pipelines are implemented in Python using Polars for efficient out-of-memory computation. The repository is structured as a reproducible project and maintained under LIDE.

ecuador-temperature-noaa: Ecuador Climate Data from NOAA

A data processing pipeline to retrieve and transform daily climate data for Ecuador from NOAA’s gridded datasets. Raw NetCDF geospatial files are converted into structured CSV outputs at the canton level, covering maximum temperature, minimum temperature, and precipitation, making the data suitable for panel regressions and environmental or health research in Ecuador. Maintained under LIDE.