print.meanEquivTest

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

Ties Bos

EquiTrends

Equivalence Testing for Pre-Trends in Difference-in-Differences
Designs

Martin Schumann

print.meanEquivTest function

<dl><dt>x</dt>
<dd>An object of class 'meanEquivTest' containing the results of the maximum test based on the bootstrap procedure.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Print meanEquivTest objects — print.meanEquivTest

<dl>

<dt>x</dt>
<dd>An object of class 'meanEquivTest' containing the results of the maximum test based on the bootstrap procedure.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

print.meanEquivTest: Print meanEquivTest objects

Description

Usage

Value

Arguments