# R Econometrics

Run IV, DiD, and RDD analyses in R with proper diagnostics

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-econometrics -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-econometrics -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-econometrics -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-econometrics -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## Description

Run IV, DiD, and RDD analyses in R with proper diagnostics

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:
1. What is your identification strategy? (IV, DiD, RDD, or simple regression)
2. What is the unit of observation? (individual, firm, country-year, etc.)
3. What fixed effects do you need? (entity, time, two-way)
4. How should standard errors be clustered?

### Step 2: Generate Analysis Code

Based on the research design, generate R code that:

1. **Uses the `fixest` package** - Modern, fast, and feature-rich for panel data
2. **Includes proper diagnostics:**
   - For IV: First-stage F-statistics, weak instrument tests
   - For DiD: Parallel trends visualization, event study plots
   - For RDD: Bandwidth selection, density tests
3. **Uses robust/clustered standard errors** appropriate for the data structure
4. **Creates publication-ready output** using `modelsummary` or `etable`

### Step 3: Structure the Output

Always include:
```r
# 1. Setup and packages
# 2. Data loading and preparation
# 3. Descriptive statistics
# 4. Main specification
# 5. Robustness checks
# 6. Visualization
# 7. Export results
```

### Step 4: Add Documentation

Include comments explaining:
- Why each specification choice was made
- Interpretation of key coefficients
- Limitations and assumptions

## Example Prompts

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/analysis/r-econometrics/SKILL.md