Result Extraction and Export

Introduction

After running an event study, you need to extract and export results for analysis, tables, and publication. This vignette covers all the ways to get results out of an EventStudyTask.

Running the Example

First, let’s set up a complete event study (using synthetic data for reproducibility):

library(EventStudy)

# Create task with synthetic data
task <- create_mock_task(n_firms = 5)  # helper for testing
params <- ParameterSet$new()
task <- run_event_study(task, params)

Extracting Results Programmatically

Abnormal Returns

# Get AR for a specific event
ar <- task$get_ar(event_id = 1)
ar
# # A tibble: 11 x 2
#    relative_index abnormal_returns
#             <dbl>            <dbl>
#  1             -5         0.00234
#  ...

Cumulative Abnormal Returns

# Get CAR for a specific event
car <- task$get_car(event_id = 1)
car
# # A tibble: 11 x 3
#    relative_index abnormal_returns      car
#             <dbl>            <dbl>    <dbl>
#  1             -5         0.00234  0.00234
#  ...

Average Abnormal Returns (AAR/CAAR)

# Get AAR/CAAR from multi-event statistics
aar <- task$get_aar(stat_name = "CSectT")
aar
# Returns the cross-sectional t-test results tibble

Model Statistics

# Get model fit statistics for a specific event
stats <- task$get_model_stats(event_id = 1)
stats$alpha
stats$beta
stats$sigma
stats$r2

The tidy() Method

The tidy.EventStudyTask() method returns results in broom-compatible long format, making it easy to use with standard tidyverse workflows.

Tidy AR

tidy_ar <- tidy.EventStudyTask(task, type = "ar")
tidy_ar
# # A tibble: 55 x 7
#    event_id group     firm_symbol term  estimate std.error statistic
#       <int> <chr>     <chr>       <chr>    <dbl>     <dbl>     <dbl>
#  1        1 TestGroup FIRM_A      -5     0.00234   0.0102     0.229
#  ...

Tidy CAR

tidy_car <- tidy.EventStudyTask(task, type = "car")
# Includes cumulative AR with window labels as terms

Tidy AAR

tidy_aar <- tidy.EventStudyTask(task, type = "aar")
# Includes AAR, CAAR, and their test statistics with p-values

Tidy Model

tidy_model <- tidy.EventStudyTask(task, type = "model")
# Returns alpha, beta, sigma, r.squared for each event

Exporting Results

Export to CSV

# Export all results (creates separate files: results_ar.csv, results_car.csv, etc.)
export_results(task, "results.csv")

# Export specific results
export_results(task, "model_stats.csv", which = "model")

Export to Excel

# Requires the openxlsx package
# Each result type gets its own worksheet
export_results(task, "results.xlsx")

Export to LaTeX

# Produces publication-ready LaTeX tables
# Requires the knitr package
export_results(task, "results.tex", which = c("model", "aar"))

Cross-Sectional Analysis of CARs

After extracting CARs, you can analyze what drives the cross-sectional variation:

# Extract CARs for all events
all_cars <- purrr::map2_dfr(
  task$data_tbl$event_id,
  task$data_tbl$firm_symbol,
  function(eid, sym) {
    car <- task$get_car(event_id = eid)
    tibble::tibble(
      event_id = eid,
      firm_symbol = sym,
      car_total = tail(car$car, 1)
    )
  }
)

# Now you can regress CARs on firm characteristics
# (see the cross_sectional_regression() function)

Visualization

# Single-event plots
plot_event_study(task, type = "ar", event_id = 1)
plot_event_study(task, type = "car", event_id = 1)

# Multi-event plots
plot_event_study(task, type = "aar")
plot_event_study(task, type = "caar")

# Model diagnostics
plot_diagnostics(task, event_id = 1)

Summary

Method	Returns	Format
`get_ar()`	AR for one event	tibble
`get_car()`	CAR for one event	tibble
`get_aar()`	AAR/CAAR for one group	tibble
`get_model_stats()`	Model fit stats	list
`tidy.EventStudyTask()`	Broom-compatible results	tibble
`export_results()`	Write to file	CSV/XLSX/LaTeX
`plot_event_study()`	Visualization	ggplot2

Simon Mueller

2026-09-06