Package: DRDID 1.2.1

Pedro H. C. SantAnna

DRDID: Doubly Robust Difference-in-Differences Estimators

Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

Authors:Pedro H. C. Sant'Anna [aut, cre, cph], Jun Zhao [aut]

DRDID_1.2.1.tar.gz
DRDID_1.2.1.zip(r-4.5)DRDID_1.2.1.zip(r-4.4)DRDID_1.2.1.zip(r-4.3)
DRDID_1.2.1.tgz(r-4.4-x86_64)DRDID_1.2.1.tgz(r-4.4-arm64)DRDID_1.2.1.tgz(r-4.3-x86_64)DRDID_1.2.1.tgz(r-4.3-arm64)
DRDID_1.2.1.tar.gz(r-4.5-noble)DRDID_1.2.1.tar.gz(r-4.4-noble)
DRDID_1.2.1.tgz(r-4.4-emscripten)DRDID_1.2.1.tgz(r-4.3-emscripten)
DRDID.pdf |DRDID.html✨
DRDID/json (API)
NEWS

# Install 'DRDID' in R:
install.packages('DRDID', repos = c('https://pedrohcgs.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pedrohcgs/drdid/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • nsw - National Supported Work Demonstration dataset
  • nsw_long - National Supported Work Demonstration dataset, in long format
  • sim_rc - Simulated repeated cross-section data

On CRAN:

8.58 score 86 stars 3 packages 127 scripts 3.2k downloads 17 exports 27 dependencies

Last updated 21 days agofrom:a346ad791a. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-win-x86_64OKOct 16 2024
R-4.5-linux-x86_64OKOct 16 2024
R-4.4-win-x86_64OKOct 16 2024
R-4.4-mac-x86_64OKOct 16 2024
R-4.4-mac-aarch64OKOct 16 2024
R-4.3-win-x86_64OKOct 16 2024
R-4.3-mac-x86_64OKOct 16 2024
R-4.3-mac-aarch64OKOct 16 2024

Exports:drdiddrdid_imp_paneldrdid_imp_rcdrdid_imp_rc1drdid_paneldrdid_rcdrdid_rc1ipw_did_panelipw_did_rcipwdidordidreg_did_panelreg_did_rcstd_ipw_did_panelstd_ipw_did_rctwfe_did_paneltwfe_did_rc

Dependencies:BHbigmemorybigmemory.sriBMiscclidata.tabledplyrfansifastglmgenericsgluelifecyclemagrittrpillarpkgconfigR6RcppRcppArmadilloRcppEigenrlangtibbletidyselecttrustutf8uuidvctrswithr

Readme and manuals

Help Manual

Help pageTopics
Locally efficient doubly robust DiD estimators for the ATTdrdid
Improved locally efficient doubly robust DiD estimator for the ATT, with panel datadrdid_imp_panel
Improved locally efficient doubly robust DiD estimator for the ATT, with repeated cross-section datadrdid_imp_rc
Improved doubly robust DiD estimator for the ATT, with repeated cross-section datadrdid_imp_rc1
Locally efficient doubly robust DiD estimator for the ATT, with panel datadrdid_panel
Locally efficient doubly robust DiD estimator for the ATT, with repeated cross-section datadrdid_rc
Doubly robust DiD estimator for the ATT, with repeated cross-section datadrdid_rc1
Inverse probability weighted DiD estimator, with panel dataipw_did_panel
Inverse probability weighted DiD estimator, with repeated cross-section dataipw_did_rc
Inverse probability weighted DiD estimators for the ATTipwdid
National Supported Work Demonstration datasetnsw
National Supported Work Demonstration dataset, in long formatnsw_long
Outcome regression DiD estimators for the ATTordid
Outcome regression DiD estimator for the ATT, with panel datareg_did_panel
Outcome regression DiD estimator for the ATT, with repeated cross-section datareg_did_rc
Simulated repeated cross-section datasim_rc
Standardized inverse probability weighted DiD estimator, with panel datastd_ipw_did_panel
Standardized inverse probability weighted DiD estimator, with repeated cross-section datastd_ipw_did_rc
Two-way fixed effects DiD estimator, with panel datatwfe_did_panel
Two-way fixed effects DiD estimator, with repeated cross-section datatwfe_did_rc