Coding & Computational Projects

Open-source coding and computational economics projects by Daniel Sánchez Pazmiño, including econometric pipelines and data infrastructure.

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Repositories with a strong coding and computational focus, ranging from econometric research pipelines to data infrastructure and computational economics coursework. Most projects are open-source on GitHub; one pipeline is available on request, as noted below.

MapleStats MCP: One MCP Server for Canadian Open Data

Canadian public data is spread across Statistics Canada tables, Census products, public use microdata files, the Bank of Canada, CMHC, federal agencies, and dozens of federal, provincial, territorial, and municipal open-data portals, each with its own API and conventions. MapleStats MCP is a Model Context Protocol server, built in Python, that gives AI assistants typed, queryable access to all of it through a single interface, in English and French. Every result carries its source, so figures can be traced back to the agency that published them. It installs from PyPI with a single uvx maplestats-mcp command and works with Claude, Codex, Cursor, and any other MCP client.

PyPI version PyPI downloads per month Python versions License: MIT CI status MCP Registry Smithery Glama score M8ven Score

EcuDataMCP: An MCP Server for Ecuador’s Open Government Data

Ecuador’s public data is abundant but scattered — household and labour surveys, the companies registry, procurement records, seismic feeds, regulatory texts — each living behind a different agency, a different API, and often Spanish-only documentation. EcuDataMCP is a Model Context Protocol server, built in Python, that collapses all of that into one interface: a single set of structured, queryable tools that let AI assistants search, filter, and analyze Ecuador’s open government data directly, without ever leaving the conversation. Under the hood it wires together CKAN’s open-data catalog, SERCOP’s public procurement records, the Instituto Geofísico’s real-time seismic and tsunami-risk feeds, the Supercías companies and financial registries, INEC’s survey and census catalog, and directories of government institutions and regulations, turning a dozen disconnected sources into one coherent, assistant-readable layer.

PyPI version PyPI downloads per month Python versions License: MIT MCP Registry Glama score M8ven Score

Research papers with R pipelines

Two of my research papers are fully reproducible R projects: the Alberta Investor Tax Credit and patent applications capstone and the temperature and presidential approval paper. Their full descriptions and citations are on the Research & Publications page.

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.

Ecuador data jobs scraping and labour-market analysis

An R-based data pipeline I built to collect and analyse public job postings for data and technology-adjacent roles in Ecuador. The scraper standardizes postings from Multitrabajos, Computrabajo, and Mipleo into a common schema, extracts job attributes and skill requirements, classifies postings into data job families, and produces repeatable cross-sections for labour-market analysis. A separate analysis repository accumulates scrape waves, deduplicates listings, and generates the figures and tables used for an El Quantificador article on the tools and technical skills demanded in Ecuador’s data labour market.