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r-econometrics

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

Category: analysis
Field: econometrics
License: Other (see repo)
Updated: 2026
Stages: data-analysis

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:
  3. For IV: First-stage F-statistics, weak instrument tests
  4. For DiD: Parallel trends visualization, event study plots
  5. For RDD: Bandwidth selection, density tests
  6. Uses robust/clustered standard errors appropriate for the data structure
  7. Creates publication-ready output using modelsummary or etable

Step 3: Structure the Output

Always include:

S
## 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

S
## ============================================
## 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:

S
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

Changelog

v1.0.0

  • Initial release with IV, DiD, RDD support