brycewang-stanford/event-study

Event Study Skill

This skill covers everything needed to conduct event studies in R - from the traditional two-way fixed effects (TWFE) approach through modern robust estimators that handle staggered treatment timing and heterogeneous treatment effects.

When to Use This Skill

Use this skill when the user wants to:

• Create event study plots (coefficient plots around a treatment event)
• Test for parallel pre-trends in a difference-in-differences design
• Estimate dynamic treatment effects
• Work with staggered treatment adoption (units treated at different times)
• Implement any of the modern DiD estimators (Callaway & Sant'Anna, Sun & Abraham, etc.)
• Diagnose problems with TWFE under treatment effect heterogeneity

Quick Decision Tree

Before writing any code, determine the user's situation:

1. Is treatment timing the same for all treated units?

   • Yes → Traditional TWFE event study is fine. See traditional-event-study.md
   • No (staggered) → Go to step 2.

2. Is there reason to expect heterogeneous treatment effects across cohorts or over time?

   • No strong reason → Traditional TWFE may still be OK, but consider robust alternatives.
   • Yes or unsure → Use a robust estimator. See modern-extensions.md

3. What is the user's goal?

   • Quick visualization of pre-trends → Traditional approach, fast and familiar.
   • Publication-quality estimation → Recommend a robust estimator + traditional as comparison.
   • Diagnostic check → Bacon decomposition to understand TWFE weights.

Core Concepts (Brief)

An event study plot displays estimated coefficients for leads (pre-treatment periods) and lags (post-treatment periods) relative to a baseline period (typically one period before treatment). The key elements are:

• Relative time: Time reindexed so that treatment occurs at period 0 for each unit.
• Baseline/reference period: Usually t = -1 (omitted from regression, normalized to zero).
• Pre-trend coefficients: Periods before treatment. If these are close to zero, it supports the parallel trends assumption.
• Post-treatment coefficients: These capture the dynamic treatment effect over time.

The parallel trends assumption states that, absent treatment, treated and control units would have followed the same trajectory. Pre-trend coefficients near zero are necessary (but not sufficient) evidence for this.

Workflow

For any event study request, follow this general workflow:

1. Understand the data: Identify the panel structure (unit ID, time variable, treatment indicator, outcome). Ask the user if not clear.
2. Determine treatment timing: Is it uniform or staggered?
3. Choose the estimator: Use the decision tree above.
4. Estimate the model: Follow the relevant reference file.
5. Create the plot: Always produce a clean, publication-ready event study plot.
6. Interpret results: Discuss pre-trends, post-treatment dynamics, and any concerns.

Reference Files

Read these as needed based on the user's situation:

• traditional-event-study.md - Traditional TWFE event study with fixest. Read this for any event study request. It covers the baseline approach, plotting, and the assumptions behind it.

• modern-extensions.md - Modern robust estimators for staggered designs. Read this when treatment timing varies across units or when the user asks about heterogeneous treatment effects, or any of the newer DiD methods.

• diagnostics-and-testing.md - Pre-trend testing, placebo checks, Bacon decomposition, sensitivity analysis. Read this when the user wants to validate their design or when you spot potential issues.

Key R Packages

| Package | Purpose | When to use | |---------|---------|-------------| | fixest | TWFE event studies, Sun & Abraham | Default starting point | | did | Callaway & Sant'Anna estimator | Staggered treatment, heterogeneous effects | | did2s | Gardner (2022) two-stage DiD | Staggered treatment, clean decomposition | | DIDmultiplegt | de Chaisemartin & D'Haultfoeuille | Staggered, robust to heterogeneity | | bacondecomp | Bacon decomposition | Diagnosing TWFE problems | | HonestDiD | Sensitivity analysis for pre-trends | Robustness checks on parallel trends | | ggplot2 | Plotting | Always | | modelsummary | Regression tables | When user needs tables |

Plotting Standards

All event study plots should follow these defaults (user can override):

• Use ggplot2 with theme_minimal() or a clean custom theme
• Include a vertical dashed line at t = 0 (treatment onset)
• Include a horizontal dashed line at y = 0 (null effect)
• IMPORTANT: Use discrete point estimates with vertical error bars (geom_point + geom_errorbar), NOT connected lines with shaded ribbons (geom_line + geom_ribbon). Each period t should show an individual point with its own error bar. This is the standard format in economics journals.
• Show 95% confidence intervals as error bars (vertical lines at each point)
• Label axes clearly: "Periods Relative to Treatment" (x) and the outcome name (y)
• Normalize the reference period coefficient to 0 explicitly
• Use colorblind-friendly palettes when comparing multiple groups
• If comparing estimators, use facets or distinct colors/shapes with a legend

Common Pitfalls to Watch For

• Forgetting to set the reference period: Always drop one pre-treatment period.
• Binning endpoint periods: With limited pre/post periods, bin the endpoints (e.g., "-5+" and "5+") to avoid small-sample noise.
• Not-yet-treated as controls: In staggered settings, TWFE uses not-yet-treated units as controls, which can introduce bias if treatment effects are heterogeneous.
• Interpreting pre-trends as proof of parallel trends: Pre-trends being zero is necessary but not sufficient. Absence of evidence is not evidence of absence.
• Unbalanced panels: Missing observations can distort event study estimates, especially at the endpoints.