mboot

This function take an influence function and use the
multiplier bootstrap to compute standard errors and critical values for
uniform confidence bands.

internal

Implements triple-difference (DDD) estimators for both average
treatment effects and event-study parameters. Methods include regression
adjustment, inverse-probability weighting, and doubly-robust estimators,
all of which rely on a conditional DDD parallel-trends assumption and
allow covariate adjustment across multiple pre- and post-treatment
periods. The methodology is detailed in Ortiz-Villavicencio and
Sant'Anna (2025) <doi:10.48550/arXiv.2505.09942>.

Marcelo Ortiz-Villavicencio

triplediff

Triple-Difference Estimators

mboot function

<dl><dt>inf_func</dt>
<dd>an influence function</dd>
<dt>did_preprocessed</dt>
<dd>A <code>dp</code> object obtained after preprocess</dd>
<dt>use_parallel</dt>
<dd>Boolean of whether or not to use parallel processing in the multiplier
bootstrap, default is <code>use_parallel=FALSE</code></dd>
<dt>cores</dt>
<dd>the number of cores to use with parallel processing, default is <code>cores=1</code></dd></dl>

Arguments

Multiplier Bootstrap — mboot

<dl>

<dt>inf_func</dt>
<dd>an influence function</dd>


<dt>did_preprocessed</dt>
<dd>A <code>dp</code> object obtained after preprocess</dd>


<dt>use_parallel</dt>
<dd>Boolean of whether or not to use parallel processing in the multiplier
bootstrap, default is <code>use_parallel=FALSE</code></dd>


<dt>cores</dt>
<dd>the number of cores to use with parallel processing, default is <code>cores=1</code></dd>

</dl>

mboot: Multiplier Bootstrap

Description

Usage

Value

Arguments