Event Studies in Stata: Finance and Economics Applications
How to implement event studies in Stata for finance and economics research — abnormal returns, CAR estimation, and visualization.
The event study is one of the oldest and most widely used methodologies in empirical finance. When a firm announces a merger, when the SEC issues a new regulation, when a CEO resigns — the event study measures whether and how much the stock price reacted. It leverages the semi-strong efficient markets hypothesis: all publicly available information is immediately incorporated into stock prices, so the price reaction at the event is the market’s assessment of the event’s value.
The methodology extends far beyond finance: economists use event study designs to measure the impact of policy changes, epidemiologists use them to study the effect of public health interventions, and political scientists use them to assess the market reaction to elections.
The Framework
An event study has three time windows:
- Estimation window: A period before the event (e.g., days -250 to -30) used to estimate “normal” returns.
- Event window: The period around the event (e.g., days -5 to +5) where you measure abnormal returns.
- Post-event window: Optional period to measure longer-term effects.
The abnormal return is the difference between the actual return and the expected return (predicted from the estimation window). A positive abnormal return means the market reacted favorably; a negative one means it didn’t.
Step 1: Data Preparation
Step 2: Expected Returns — The Market Model
For multi-event studies (many firms, each with their own event date), loop over firms:
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Get Early AccessStep 3: Cumulative Abnormal Returns (CAR)
Step 4: Visualization
Testing Significance
Standard approaches:
- Cross-sectional t-test: Compute CAR for each firm over the event window, then test whether the mean CAR is different from zero:
ttest car = 0 - Boehmer, Musumeci, and Poulsen (1991) test: Standardizes each firm’s AR by its estimation-window standard deviation before averaging. More powerful when event-window variance varies across firms.
- Kolari and Pynnönen (2010) adjusted test: Corrects for cross-sectional correlation when events cluster in calendar time (e.g., all firms affected by the same regulation on the same day).
Using estudy Package
estudy handles the estimation window, market model, AR/CAR computation, and significance testing in one command. It’s useful for standard event studies but less flexible for custom specifications.
How Sytra Handles Event Studies
Tell Sytra: “Run an event study for merger announcements. Estimation window is [-250, -30]. Event window is [-5, +5]. Market model with CRSP value-weighted returns.”
Sytra generates the full pipeline — data prep, firm-by-firm market model estimation, AR/CAR computation, cross-sectional test, and the event study plot — in one loop. It flags if the estimation window has too few observations for any firm and handles firms with missing trading days automatically.