R Econometrics
This generates publication-ready econometrics code in R for causal inference work. It handles IV, DiD, and RDD analyses using the fixest package, which is faster and cleaner than base lm() for panel data. The output includes proper diagnostics like first-stage F-stats for IV and parallel trends plots for DiD, plus it sets up clustered standard errors at the right level. Built by the Awesome Econ AI Community, it's aimed at economists who want rigorous code structure without reinventing the wheel each time. The event study implementation and two-way fixed effects handling are solid, though you'll still need to understand your identification strategy before asking it to generate anything.
- skills.sh installs
- 337
- GitHub stars
- 668
- last updated
- Aug 21, 2026
Install
$ npx -y skills add meleantonio/awesome-econ-ai-stuff --skill r-econometrics --agent claude-codeInstalls into .claude/skills of the current project.
Files
R Econometrics
Purpose
This skill helps economists run rigorous econometric analyses in R, including Instrumental Variables (IV), Difference-in-Differences (DiD), and Regression Discontinuity Design (RDD). It generates publication-ready code with proper diagnostics and robust standard errors.
When to Use
- Running causal inference analyses
- Estimating treatment effects with panel data
- Creating publication-ready regression tables
- Implementing modern econometric methods (two-way fixed effects, event studies)
Instructions
Step 1: Understand the Research Design
Before generating code, ask the user:
- What is your identification strategy? (IV, DiD, RDD, or simple regression)
- What is the unit of observation? (individual, firm, country-year, etc.)
- What fixed effects do you need? (entity, time, two-way)
- How should standard errors be clustered?
Step 2: Generate Analysis Code
Based on the research design, generate R code that:
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- Repository
- github.com/meleantonio/awesome-econ-ai-stuff
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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