0. Installation | 1. Prepare Data | 2. Estimate DiD for a Single Cohort | 3. Estimate DiD for All Cohorts and Event Times
Package: DiDforBigData 1.0.0.9000
DiDforBigData: A Big Data Implementation of Difference-in-Differences Estimation with Staggered Treatment
Provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts.
Authors:
DiDforBigData_1.0.0.9000.tar.gz
DiDforBigData_1.0.0.9000.zip(r-4.7-any)DiDforBigData_1.0.0.9000.zip(r-4.6-any)DiDforBigData_1.0.0.9000.zip(r-4.5-any)
DiDforBigData_1.0.0.9000.tgz(r-4.6-any)DiDforBigData_1.0.0.9000.tgz(r-4.5-any)
DiDforBigData_1.0.0.9000.tar.gz(r-4.7-any)DiDforBigData_1.0.0.9000.tar.gz(r-4.6-any)
DiDforBigData_1.0.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DiDforBigData/json (API)
| # Install 'DiDforBigData' in R: |
| install.packages('DiDforBigData', repos = c('https://setzler.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/setzler/didforbigdata/issues
Pkgdown/docs site:https://setzler.github.io
Last updated from:968c2eb378. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 133 | ||
| source / vignettes | OK | 220 | ||
| linux-release | NOTE | 132 | ||
| macos-release | NOTE | 181 | ||
| macos-oldrel | NOTE | 149 | ||
| windows-devel | NOTE | 68 | ||
| windows-release | NOTE | 66 | ||
| windows-oldrel | NOTE | 86 | ||
| wasm-release | OK | 123 |
Dependencies:data.tablelatticesandwichzoo
Get Started
Last update: 2023-08-26
Started: 2022-10-26
Theory
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
Last update: 2023-04-05
Started: 2022-10-27
Examples
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
Last update: 2023-04-05
Started: 2022-10-26
Background
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
Last update: 2023-03-24
Started: 2023-03-24
