r-econometrics

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Audit 0.4.1 · source sha 369fe15b2a25 · 21 Oct 2026Report an issue or request a re-scan

• Publishermeleantonio GitHub userOwner of the repository this skill is read from
• Source foldermeleantonio/awesome-econ-ai-stuff/_skills/analysis/r-econometricsThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict369fe15b2a25A new audit runs when the source changes
• Repository statusNot archivedLast push 25 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

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

---
name: r-econometrics
description: Run IV, DiD, and RDD analyses in R with proper diagnostics
workflow_stage: analysis
compatibility:
  - claude-code
  - cursor
  - codex
  - gemini-cli
author: Awesome Econ AI Community
version: 1.0.0
tags:
  - R
  - econometrics
  - causal-inference
  - fixest
  - regression
---

# 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

- "Run a DiD analysis with state and year fixed effects, clustering at the state level"
- "Estimate the effect of X on Y using Z as an instrument"
- "Create an event study plot showing treatment effects by year"
- "Run a sharp RDD with optimal bandwidth selection"

## Example Output

```r
# ============================================
# Difference-in-Differences Analysis
# ============================================

# Setup
library(tidyverse)
library(fixest)
library(modelsummary)

# Load data
df <- read_csv("data.csv")

# Prepare treatment variable
df <- df %>%
  mutate(
    post = year >= treatment_year,
    treated = state %in% treatment_states,
    treat_post = treated * post
  )

# ----------------------------------------
# Main DiD Specification
# ----------------------------------------

# Two-way fixed effects
did_model <- feols(
  outcome ~ treat_post | state + year,
  data = df,
  cluster = ~state
)

# View results
summary(did_model)

# ----------------------------------------
# Event Study
# ----------------------------------------

# Create relative time variable
df <- df %>%
  mutate(rel_time = year - treatment_year)

# Event study regression
event_study <- feols(
  outcome ~ i(rel_time, treated, ref = -1) | state + year,
  data = df,
  cluster = ~state
)

# Plot coefficients
iplot(event_study, 
      main = "Event Study: Effect on Outcome",
      xlab = "Years Relative to Treatment")

# ----------------------------------------
# Robustness: Alternative Specifications
# ----------------------------------------

# Different clustering
did_robust <- feols(
  outcome ~ treat_post | state + year,
  data = df,
  cluster = ~state + year  # Two-way clustering
)

# ----------------------------------------
# Export Results
# ----------------------------------------

modelsummary(
  list("Main" = did_model, "Two-way Cluster" = did_robust),
  stars = c('*' = 0.1, '**' = 0.05, '***' = 0.01),
  output = "results/did_table.tex"
)
```

## Requirements

### Software
- R 4.0+

### Packages
- `fixest` - Fast fixed effects estimation
- `modelsummary` - Publication-ready tables
- `tidyverse` - Data manipulation
- `ggplot2` - Visualization

Install with:
```r
install.packages(c("fixest", "modelsummary", "tidyverse"))
```

## Best Practices

1. **Always cluster standard errors** at the level of treatment assignment
2. **Run pre-trend tests** for DiD designs
3. **Report first-stage F-statistics** for IV (should be > 10)
4. **Use `feols` over `lm`** for panel data (faster and more features)
5. **Document all specification choices** in your code comments

## Common Pitfalls

- ❌ Not clustering standard errors at the right level
- ❌ Ignoring weak instruments in IV estimation
- ❌ Using TWFE with staggered treatment timing (use `did` or `sunab()` instead)
- ❌ Not reporting robustness checks

## References

- [fixest documentation](https://lrberge.github.io/fixest/)
- [Cunningham (2021) Causal Inference: The Mixtape](https://mixtape.scunning.com/)
- [Angrist & Pischke (2009) Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)

## Changelog

### v1.0.0
- Initial release with IV, DiD, RDD support

Excerpt, first 5,318 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-21
   ✓SAFE B 89 / 100

   first audit · source 369fe15b2a25

Questions about r-econometrics

• github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/a — the audited source at commit 369fe15b2a25 and every finding aboveObserved · 21 Oct 2026

Spotted something wrong? Report a correction · Methodology