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

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Difference-in-Differences Analysis

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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 )

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Main DiD Specification

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Two-way fixed effects

did_model <- feols ( outcome ~ treat_post | state + year , data = df , cluster = ~ state )

View results

summary ( did_model )

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Event Study

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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" )

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Robustness: Alternative Specifications

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Different clustering

did_robust <- feols ( outcome ~ treat_post | state + year , data = df , cluster = ~ state + year

Two-way clustering

)

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Export Results

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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