Introduction | Requirements | Basic Usage | Customizing the Report | Title and Author | Selecting Sections | Summary Only | Including Cross-Sectional Analysis | Output Formats | Panel Event Study Reports | Programmatic Report Generation

Introduction | The ModelBase Interface | Example: Industry-Adjusted Model | Using the Custom Model | Example: Excess Return Model | Using Factor Models | Key Points

Introduction | The TestStatisticBase Interface | Example: Wilcoxon Signed-Rank Test (Single Event) | Example: Kolmogorov-Smirnov Test (Multi-Event) | Registering Custom Statistics | Using Custom Statistics in the Pipeline | Built-in Test Statistics | Key Points

Introduction | Running the Event Study First | Cross-Sectional Regression | Basic Usage | Robust Standard Errors | Custom CAR Window | Accessing the Underlying Data | Group Comparisons | car_by_group() | Custom CAR Window for Groups | CAR Quantiles | Visualizing the CAR Distribution | Histogram | Histogram by Group | A Complete Cross-Sectional Workflow | Tips and Best Practices | References

Introduction | Setup: Running an Event Study | Step 1: Validate the Task | What It Checks | 1. Event Date Exists | 2. Sufficient Estimation Window | 3. Time Series Gaps | 4. Window Overlap | 5. Thin Trading | Using Validation Results | Step 2: Model Diagnostics | Diagnostic Tests Explained | Shapiro-Wilk Normality Test (shapiro_p) | Durbin-Watson Statistic (dw_stat) | Ljung-Box Test (ljung_box_p) | First-Order Autocorrelation (acf1) | Sigma and R-squared (sigma, r2) | Diagnostics for a Single Event | Interpreting Diagnostics as a Table | Step 3: Visual Diagnostics | Comparing Diagnostics Across Events | Step 4: Pre-trend Testing | Interpreting Results | Pre-trend Test by Group | What to Do When Pre-trends Are Detected | A Complete Diagnostic Workflow | Decision Guide | References

Introduction | Downloading Stock Data | Multiple Stocks | Format for EventStudyTask | Raw Format | Downloading Factor Data | Fama-French 3-Factor | Fama-French 5-Factor | Monthly Frequency | Using with Factor Models | Downloading Risk-Free Rate | Column Name Mapping

Introduction | Factor Data: The factor_tbl | Required Columns by Model | Sourcing Factor Data | Fama-French Three-Factor Model | Running the Study | Inspecting Factor Loadings | Fama-French Five-Factor Model | Carhart Four-Factor Model | GARCH(1,1) Model | Requirements | GARCH-Specific Results | When to Use GARCH | Buy-and-Hold Abnormal Returns (BHAR) | Motivation | Running a BHAR Study | Interpreting BHAR Results | BHAR vs. CAR: When Does It Matter? | Comparing Models | Summary | References

Introduction | HAC Standard Errors | Kolari-Pynnonen Test | Wild Bootstrap Inference | Multiple Testing Corrections | Combining Approaches | References

Introduction | Data Structure | The IntradayEventStudyTask | Non-Parametric Test | Interpreting Results | Plotting Results | References

Introduction | Quick Start | Example: Dieselgate | Initialization | Define the Event Study | Execute the Event Study | Visualize the Results | Cumulative Abnormal Return (CAR) | Cumulative Average Abnormal Return (CAAR) | Next Steps | Conclusion

Introduction | Data Setup | TWFE (Default) | Sun-Abraham | Callaway-Sant'Anna | de Chaisemartin-D'Haultfoeuille | Borusyak-Jaravel-Spiess | Comparing Estimators | When to Use Which? | References

Introduction | Data Structure (Miller Section I) | Dataset A: Single-Cohort Design | Dataset B: Staggered Multi-Cohort Design | Creating Tasks | The Estimating Equation (Miller Section II) | Static TWFE: The Single-Coefficient Model (Miller Section II.A) | Dynamic TWFE: Event-Time Coefficients (Miller Section II.B) | Reference Period and Normalization (Miller Section III) | Endpoint Binning (Miller Section IV) | Staggered Treatment and Heterogeneous Effects (Miller Section V) | The TWFE Problem | Sun & Abraham (2021) Interaction-Weighted Estimator | Comparing TWFE and Sun & Abraham | Pre-trend Testing (Miller Section VI) | Visual Inspection | Joint Test for Pre-trends | Miller's Caution | Reporting and Counterfactuals (Miller Section VII) | Statistical Inference (Miller Section VIII) | Clustered Standard Errors | How Many Clusters Are Enough? | Practical Checklist | References

Introduction | Running the Example | Extracting Results Programmatically | Abnormal Returns | Cumulative Abnormal Returns | Average Abnormal Returns (AAR/CAAR) | Model Statistics | The tidy() Method | Tidy AR | Tidy CAR | Tidy AAR | Tidy Model | Exporting Results | Export to CSV | Export to Excel | Export to LaTeX | Cross-Sectional Analysis of CARs | Visualization | Summary

Introduction | Basic Usage | Power Analysis | Varying Effect Size | Varying Sample Size | Customizing the DGP | Different Test Statistics | Rejection by Day | Seed Reproducibility

Purpose | Audit methodology | Return models | Market Model (MacKinlay 1997) | Market Adjusted Model | Comparison Period Mean Adjusted Model | Fama-French Three-Factor Model | Fama-French Five-Factor Model | Carhart Four-Factor Model | Custom Model | BHAR Model (Barber-Lyon 1997) | Volume Model | Volatility Model | Rolling-Window Model | GARCH Model | DCC-GARCH Model | Test statistics | Multi-event all-NA exclusion (2026-09-24 re-evaluation, item A2) | AR / CAR t-tests (ARTTest, CARTTest) | BHAR t-test (BHARTTest) | Cross-Sectional t-test (CSectTTest) | Patell Z-test (PatellZTest) | BMP test (BMPTest) | Sign test (SignTest) | Generalized Sign test (GeneralizedSignTest) | Rank test (RankTest) | Calendar-Time Portfolio test (CalendarTimePortfolioTest) | Kolari-Pynnonen test (KolariPynnonenTest) | PermutationTest (unwired stub) | TestStatisticBase confidence_type (2026-09-24 re-evaluation, item A10) | Cross-sectional regression (2026-09-24 re-evaluation, item A5) | Wild bootstrap inference (2026-09-24 re-evaluation, item A12) | Audit log | Audit summary

Introduction | Data Setup | Creating the Task | Estimation | quadprog (default) | optim (fallback) | Results | Visualization | Trajectory Plot | Gap Plot | Placebo Plot | Placebo Test | References

Why the advisor exists | How it works: two layers | The multi-automaker dieselgate example | VW's single-firm crash | Seeing the crash: VW abnormal returns with confidence interval | VW cumulative abnormal return with confidence band | Multi-group results: idiosyncratic vs. contagion | Group CAAR comparison plot | What runs offline (deterministic) | Diagnostics | Recommended statistics | Robustness flags | What the LLM adds (shown static) | Why this block is not evaluated at build time | Summary

Introduction | Rolling Window Model | Basic Usage | Custom Window Size | Diagnostics | How it Works | DCC-GARCH Model | Requirements | Custom GARCH Orders | Convergence Notes | Comparison

Introduction | Data Requirements | Volume Event Study | Volatility Event Study | Setup | Running the Volume Study | Log Transform | Interpreting Results | Visualization | Running the Volatility Study | Combining Price, Volume, and Volatility | Diagnostics | Export | References

Introduction | Requirements | Basic Usage | Customizing the Report | Title and Author | Selecting Sections | Summary Only | Including Cross-Sectional Analysis | Output Formats | Panel Event Study Reports | Programmatic Report Generation

Introduction | Data Setup | TWFE (Default) | Sun-Abraham | Callaway-Sant'Anna | de Chaisemartin-D'Haultfoeuille | Borusyak-Jaravel-Spiess | Comparing Estimators | When to Use Which? | References

Purpose | Audit methodology | Return models | Market Model (MacKinlay 1997) | Market Adjusted Model | Comparison Period Mean Adjusted Model | Fama-French Three-Factor Model | Fama-French Five-Factor Model | Carhart Four-Factor Model | Custom Model | BHAR Model (Barber-Lyon 1997) | Volume Model | Volatility Model | Rolling-Window Model | GARCH Model | DCC-GARCH Model | Test statistics | Multi-event all-NA exclusion (2026-09-24 re-evaluation, item A2) | AR / CAR t-tests (ARTTest, CARTTest) | BHAR t-test (BHARTTest) | Cross-Sectional t-test (CSectTTest) | Patell Z-test (PatellZTest) | BMP test (BMPTest) | Sign test (SignTest) | Generalized Sign test (GeneralizedSignTest) | Rank test (RankTest) | Calendar-Time Portfolio test (CalendarTimePortfolioTest) | Kolari-Pynnonen test (KolariPynnonenTest) | PermutationTest (unwired stub) | TestStatisticBase confidence_type (2026-09-24 re-evaluation, item A10) | Cross-sectional regression (2026-09-24 re-evaluation, item A5) | Wild bootstrap inference (2026-09-24 re-evaluation, item A12) | Audit log | Audit summary

Introduction | Quick Start | Example: Dieselgate | Initialization | Define the Event Study | Execute the Event Study | Visualize the Results | Cumulative Abnormal Return (CAR) | Cumulative Average Abnormal Return (CAAR) | Next Steps | Conclusion

Introduction | Downloading Stock Data | Multiple Stocks | Format for EventStudyTask | Raw Format | Downloading Factor Data | Fama-French 3-Factor | Fama-French 5-Factor | Monthly Frequency | Using with Factor Models | Downloading Risk-Free Rate | Column Name Mapping

Why the advisor exists | How it works: two layers | The multi-automaker dieselgate example | VW's single-firm crash | Seeing the crash: VW abnormal returns with confidence interval | VW cumulative abnormal return with confidence band | Multi-group results: idiosyncratic vs. contagion | Group CAAR comparison plot | What runs offline (deterministic) | Diagnostics | Recommended statistics | Robustness flags | What the LLM adds (shown static) | Why this block is not evaluated at build time | Summary

Introduction | HAC Standard Errors | Kolari-Pynnonen Test | Wild Bootstrap Inference | Multiple Testing Corrections | Combining Approaches | References

Introduction | Basic Usage | Power Analysis | Varying Effect Size | Varying Sample Size | Customizing the DGP | Different Test Statistics | Rejection by Day | Seed Reproducibility

Introduction | Data Setup | Creating the Task | Estimation | quadprog (default) | optim (fallback) | Results | Visualization | Trajectory Plot | Gap Plot | Placebo Plot | Placebo Test | References

Introduction | Rolling Window Model | Basic Usage | Custom Window Size | Diagnostics | How it Works | DCC-GARCH Model | Requirements | Custom GARCH Orders | Convergence Notes | Comparison

Motivation | Cox Proportional Hazard Model | Available Models | Linear Regression | Logistic Regression | Cox Proportional Hazards Regression | Random Forests | Case Based Reasoning | Search for Similar Cases | Check Proportional Hazard Assumption | Distance Matrix Calculation

Objective | Define a Distance Measure for Numerical and Categorical Features | Weighted Distance Measure | Statistical Model | Random Forests | Proximity Measure | Depth Measure: A modified proximity measure

Introduction | Data Structure (Miller Section I) | Dataset A: Single-Cohort Design | Dataset B: Staggered Multi-Cohort Design | Creating Tasks | The Estimating Equation (Miller Section II) | Static TWFE: The Single-Coefficient Model (Miller Section II.A) | Dynamic TWFE: Event-Time Coefficients (Miller Section II.B) | Reference Period and Normalization (Miller Section III) | Endpoint Binning (Miller Section IV) | Staggered Treatment and Heterogeneous Effects (Miller Section V) | The TWFE Problem | Sun & Abraham (2021) Interaction-Weighted Estimator | Comparing TWFE and Sun & Abraham | Pre-trend Testing (Miller Section VI) | Visual Inspection | Joint Test for Pre-trends | Miller's Caution | Reporting and Counterfactuals (Miller Section VII) | Statistical Inference (Miller Section VIII) | Clustered Standard Errors | How Many Clusters Are Enough? | Practical Checklist | References

Introduction | Running the Event Study First | Cross-Sectional Regression | Basic Usage | Robust Standard Errors | Custom CAR Window | Accessing the Underlying Data | Group Comparisons | car_by_group() | Custom CAR Window for Groups | CAR Quantiles | Visualizing the CAR Distribution | Histogram | Histogram by Group | A Complete Cross-Sectional Workflow | Tips and Best Practices | References

Introduction | Setup: Running an Event Study | Step 1: Validate the Task | What It Checks | 1. Event Date Exists | 2. Sufficient Estimation Window | 3. Time Series Gaps | 4. Window Overlap | 5. Thin Trading | Using Validation Results | Step 2: Model Diagnostics | Diagnostic Tests Explained | Shapiro-Wilk Normality Test (shapiro_p) | Durbin-Watson Statistic (dw_stat) | Ljung-Box Test (ljung_box_p) | First-Order Autocorrelation (acf1) | Sigma and R-squared (sigma, r2) | Diagnostics for a Single Event | Interpreting Diagnostics as a Table | Step 3: Visual Diagnostics | Comparing Diagnostics Across Events | Step 4: Pre-trend Testing | Interpreting Results | Pre-trend Test by Group | What to Do When Pre-trends Are Detected | A Complete Diagnostic Workflow | Decision Guide | References

Introduction | Factor Data: The factor_tbl | Required Columns by Model | Sourcing Factor Data | Fama-French Three-Factor Model | Running the Study | Inspecting Factor Loadings | Fama-French Five-Factor Model | Carhart Four-Factor Model | GARCH(1,1) Model | Requirements | GARCH-Specific Results | When to Use GARCH | Buy-and-Hold Abnormal Returns (BHAR) | Motivation | Running a BHAR Study | Interpreting BHAR Results | BHAR vs. CAR: When Does It Matter? | Comparing Models | Summary | References

Introduction | Data Requirements | Volume Event Study | Volatility Event Study | Setup | Running the Volume Study | Log Transform | Interpreting Results | Visualization | Running the Volatility Study | Combining Price, Volume, and Volatility | Diagnostics | Export | References

Introduction | Data Structure | The IntradayEventStudyTask | Non-Parametric Test | Interpreting Results | Plotting Results | References

Introduction | The ModelBase Interface | Example: Industry-Adjusted Model | Using the Custom Model | Example: Excess Return Model | Using Factor Models | Key Points

Introduction | The TestStatisticBase Interface | Example: Wilcoxon Signed-Rank Test (Single Event) | Example: Kolmogorov-Smirnov Test (Multi-Event) | Registering Custom Statistics | Using Custom Statistics in the Pipeline | Built-in Test Statistics | Key Points

Introduction | Running the Example | Extracting Results Programmatically | Abnormal Returns | Cumulative Abnormal Returns | Average Abnormal Returns (AAR/CAAR) | Model Statistics | The tidy() Method | Tidy AR | Tidy CAR | Tidy AAR | Tidy Model | Exporting Results | Export to CSV | Export to Excel | Export to LaTeX | Cross-Sectional Analysis of CARs | Visualization | Summary