R Econometrics
- 290 installs
- 590 repo stars
- Updated June 23, 2026
- meleantonio/awesome-econ-ai-stuff
Implement R econometric models—panel regressions, IV, DiD, time series—and reproducible pipelines when estimating causal or predictive relationships in economic data.
About
Guides Claude through R econometrics implementation: load and tidy economic datasets, specify models with appropriate fixed effects or instruments, run diagnostics, and generate reproducible estimates and tables aligned with economics research standards.
- R workflows for causal and panel econometrics
- Data cleaning, formula specs, and diagnostics
- Reproducible scripts for tables and robustness checks
- Economics-domain conventions for inference reporting
R Econometrics by the numbers
- 290 all-time installs (skills.sh)
- +9 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #596 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 290 |
|---|---|
| repo stars | ★ 590 |
| Last updated | June 23, 2026 |
| Repository | meleantonio/awesome-econ-ai-stuff ↗ |
What it does
Implement R econometric models—panel regressions, IV, DiD, time series—and reproducible pipelines when estimating causal or predictive relationships in economic data.
Files
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:
# 1. Setup and packages
# 2. Data loading and preparation
# 3. Descriptive statistics
# 4. Main specification
# 5. Robustness checks
# 6. Visualization
# 7. Export resultsStep 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 estimationmodelsummary- Publication-ready tablestidyverse- Data manipulationggplot2- Visualization
Install with:
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
didorsunab()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
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:
# 1. Setup and packages
# 2. Data loading and preparation
# 3. Descriptive statistics
# 4. Main specification
# 5. Robustness checks
# 6. Visualization
# 7. Export resultsStep 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 estimationmodelsummary- Publication-ready tablestidyverse- Data manipulationggplot2- Visualization
Install with:
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
didorsunab()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
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