Analyzing Event Impacts with Stata: AR and CAR
Event studies are a powerful tool in finance to assess the impact of specific events on a company’s stock price. Stata, a widely used statistical software, provides robust capabilities for conducting event studies using Autoregressive (AR) models and calculating Cumulative Abnormal Returns (CAR). This article explores how to leverage Stata for these analyses.
Understanding AR Models
An AR model is a time series model that explains the current value of a variable (e. g., stock return) by its own lagged values (past returns) and an error term. The number of past returns included in the model is denoted as the AR order (p). AR models are helpful in event studies because they capture the inherent time-dependence of stock returns.
Calculating Abnormal Returns (ARs)
Abnormal returns (ARs) represent the unexpected
ar return L.return10, lags(1 2) // estimate AR(2) model with lagged returns
predict uhat, residuals // obtain residuals (unexpected returns)
In this example, is the variable containing daily stock returns, lags the variable by 10 days (to account for non-stationarity), and specifies an AR(2) model with lags of 1 and 2 days. The command saves the residuals as unexpected returns in the variable . return L.return10lags(1 2)predictuhat
Cumulative Abnormal Returns (CAR)
Cumulative Abnormal Returns (CAR) summarize the abnormal performance of a stock over the entire event window. CAR is calculated by summing the abnormal returns for each day within the window. A positive CAR indicates an overall positive market reaction to the event, while a negative CAR suggests a negative reaction.
Stata Commands for CAR
Stata offers specialized commands for event studies, including eventstudyand mera. The eventstudycommand allows for a more comprehensive Hong Kong Phone Numbers event study analysis. However, for calculating basic CAR, we can use the meracommand. Here’s an example:
mera return, event(0) window(-2 2) // calculate CAR with event day as day 0
This syntax calculates the CAR for the variable returnwith the event day set as day 0. The window(-2 2)specifies the event window from 2 days before the event to 2 days after.
The ResultsStata provides various outputs
Event studies,including coefficient estimates for the overall CAR.We should analyze the significance of AR coefficients Australia Phone Number List and the statistical significance of the CAR to draw conclusions about the event’s impact.Additionally,it’s helpful to plot the CAR over the event window to visualize the market reaction over time.
Beyond the Basics
Stata offers advanced features for event studies,including:
Multiple event windows:Analyze multiple event windows to capture short-term and long-term effects.
Market adjustment models:Account for broader market movements by including market return as an additional variable in the AR model.
Cross-sectional analysis:Compare the CARs of multiple firms experiencing similar events.
Conclusion
