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nonlinear_attgt(): Core estimator for group-time
ATT(g,t) under logit, probit, Poisson, negative binomial, and linear
outcome models with staggered treatment adoption.
nonlinear_aggte(): Aggregation into event-study
(dynamic), group-level, calendar-time, and overall ATT
estimates.
nonlinear_pretest(): Pre-treatment parallel trends
test with joint chi-squared test and HonestDiD-style sensitivity
analysis.
binary_did_logit() /
binary_did_probit(): Simple 2×2 DiD with binary outcomes on
the log-odds / probit scale, with APE reporting.
binary_did_dr(): Doubly-robust binary DiD combining
logit outcome regression with inverse probability weighting.
count_did_poisson(): Poisson QMLE DiD for count
outcomes following Wooldridge (2023), reporting rate ratios.
odds_ratio_did(): Odds-ratio DiD estimator
(scale-free, symmetric).
nonlinear_bounds(): Nonparametric Manski bounds and
PT-restricted bounds for binary outcomes.
sim_binary_panel() / sim_count_panel():
Data-generating processes for simulation studies with staggered
treatment and heterogeneous effects.
S3 methods: print(), summary(),
plot() for all main object classes.
Rcpp-accelerated bootstrap weight generation and DR score computation.
This is version 0.1.0 — an initial implementation of a methodology that is actively being developed in the econometrics literature. The core identification arguments follow Roth & Sant’Anna (2023) and Wooldridge (2023). Standard errors are based on influence function / sandwich estimators.
Known limitations: - Simultaneous confidence bands use a conservative
normal approximation; exact bands require the multiplier bootstrap
(boot = TRUE). - Negative binomial staggered DiD uses
approximation; full MLE version is planned for v0.2.0.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.