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:

• Uses the fixest package - Modern, fast, and feature-rich for panel data

• 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

• Uses robust/clustered standard errors appropriate for the data structure

• Creates publication-ready output using modelsummary or etable

Step 3: Structure the Output

Always include:

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

============================================

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:

install.packages(c("fixest", "modelsummary", "tidyverse"))

Best Practices

• Always cluster standard errors at the level of treatment assignment

• Run pre-trend tests for DiD designs

• Report first-stage F-statistics for IV (should be > 10)

• Use feols over lm for panel data (faster and more features)

• 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

• Cunningham (2021) Causal Inference: The Mixtape

• Angrist & Pischke (2009) Mostly Harmless Econometrics

Changelog

v1.0.0

• Initial release with IV, DiD, RDD support