NonlinearDiD 0.2.0
- Added support for repeated cross-section staggered DiD designs.
- Added
data_type = "panel" and
data_type = "repeated_cross_section" options.
- Made
idname optional for repeated cross-section
designs.
- Added support for sampling weights through
weightsname.
- Added support for clustered inference through
cluster_var.
- Added examples for binary repeated cross-section outcomes with
staggered treatment timing.
- Preserved all panel-data functionality from version 0.1.0.
Implementation notes
- Repeated cross-section ATT(g,t) uses the Wooldridge (2023) pooled
QMLE with a treatment-by-period interaction. The doubly-robust variant
augments this with inverse probability weighting on the estimated
propensity score.
- Sampling weights (when supplied via
weightsname) are
used throughout: the outcome regression, the propensity score model, and
the pooled QMLE. They are multiplied with the IPW factor in the
doubly-robust path.
- Analytical SEs for the RCS path use
sandwich::vcovCL
when cluster_var is supplied, sandwich::vcovHC
(HC1) otherwise. Panel SEs continue to use the influence-function
approach from v0.1.0; set boot = TRUE for fully clustered
panel inference.
- The bootstrap automatically resamples whole clusters when
cluster_var is provided, units when
data_type = "panel", or individual rows when
data_type = "repeated_cross_section" without
clustering.
- The compiled C++ helpers from v0.1.0 have been replaced with
equivalent pure-R implementations, eliminating the Rcpp dependency. The
package no longer requires compilation.
Bug fixes
NonlinearDiD 0.1.0
- Initial CRAN release.
- Panel-data ATT(g,t) estimation under logit, probit, Poisson,
negative binomial, and linear outcome models.
- Doubly-robust estimator combining outcome regression and propensity
score weighting.
nonlinear_aggte(): event-study, group, calendar, and
simple aggregations.nonlinear_pretest(): joint chi-squared and individual
pre-trend tests.nonlinear_bounds(): Manski and
parallel-trends-constrained bounds.binary_did_logit(), binary_did_probit(),
binary_did_dr(): 2x2 binary DiD estimators.count_did_poisson(): Poisson QMLE DiD.odds_ratio_did(): scale-free odds-ratio DiD.sim_binary_panel(), sim_count_panel():
simulation utilities.