0. Installation | 1. Prepare Data | 2. Estimate DiD for a Single Cohort | 3. Estimate DiD for All Cohorts and Event Times

1. Conceptual Framework | 1.1 Notation and Key Concepts | 1.2 Goal | 1.3 Difference-in-differences | 2. Identification | 2.1 Identifying Assumptions | 2.2 Proof of Identification by DiD | 3. The DiDge(...) Command | 3.1 Automatic Control Group Selection | 3.2 DiD Estimation for a Single $(g,e)$ Combination | 4. The DiD(...) Command | 4.1 DiD Estimation for All Possible $(g,e)$ Combinations | 4.2 Estimate the Average DiD across Cohorts and Event Times

1. Choosing the Control Group | The control_group argument | Example | 2. Avoiding anticipation | The base_event argument | 3. Controlling for Time-varying Covariates | The covariate_names entry | 4. Controlling for Time-varying Fixed-effects | The fixedeffect_names entry | 5. Robust and Clustered Standard Errors | The cluster_names entry | 6. Parallelization | The parallel_cores argument | 7. Average across Event Times | The Esets argument

0. Installation | 1. Character rows | 2. Numeric rows | 3. Custom Formatting using Surround | 4. Multicolumn Rows | 5. Combine Rows | Combine Rows Side-by-side | Stack Rows Vertically | Order of Operations: Horizontal Comes First | 6. Vertical Space between Rows | 7. Midrule and Partial Midrules | 8. Save a LaTeX table in .tex format | 9. Save LaTeX Table and Compile to PDF

1. Why do we need a staggered DiD package for big data? | 2. This package | 3. Demonstration | 3.1 Point estimates | 3.2 Speed test | 3.3 Memory test

1. Rounding | 2. Percentage | 3. Dollar Sign | 4. Standard Error | 5. p-values