weighted_mean_cpp

Computes weighted mean efficiently for bootstrap.

internal

Implements difference-in-differences estimators for staggered
treatment adoption with binary, count, and other nonlinear outcomes.
Extends Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>
to handle the fundamental identification challenges that arise with nonlinear
outcome models (logit, probit, Poisson) in heterogeneous treatment timing
designs. Provides group-time average treatment effects on the treated (ATT),
aggregation schemes, and pre-treatment parallel trends tests appropriate for
nonlinear settings. Methods include doubly-robust semiparametric estimators,
nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>, Roth and Sant'Anna (2023) <doi:10.3982/ECTA19255>, and Wooldridge (2023) <doi:10.1093/ectj/utad016>.

Subir Hait

NonlinearDiD

Staggered Difference-in-Differences with Nonlinear Outcomes

weighted_mean_cpp function

<dl><dt>x</dt>
<dd>Numeric vector.</dd>
<dt>w</dt>
<dd>Numeric weights vector.</dd></dl>

Arguments

Weighted mean (fast) — weighted_mean_cpp

<dl>

<dt>x</dt>
<dd>Numeric vector.</dd>


<dt>w</dt>
<dd>Numeric weights vector.</dd>

</dl>

weighted_mean_cpp: Weighted mean (fast)

Description

Usage

Value

Arguments