meanTest_optim_func

<code>meanTest_optim_func</code> solves the optimization problem to find the minimum equivalence threshold for which one can reject the null hypothesis of non-negligible pre-trend differences at a given significance level for the equivalence test based on the mean placebo coefficient.

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

meanTest_optim_func function

<dl><dt>coef</dt>
<dd>The estimated absolute value of the mean placebo coefficients</dd>
<dt>sd</dt>
<dd>The estimated standard deviation of the mean of the placebo coefficients</dd>
<dt>alpha</dt>
<dd>The significance level</dd></dl>

Arguments

Finding the minimum equivalence threshold for the mean equivalence test — meanTest_optim_func

<dl>

<dt>coef</dt>
<dd>The estimated absolute value of the mean placebo coefficients</dd>


<dt>sd</dt>
<dd>The estimated standard deviation of the mean of the placebo coefficients</dd>


<dt>alpha</dt>
<dd>The significance level</dd>

</dl>

meanTest_optim_func: Finding the minimum equivalence threshold for the mean equivalence test

Description

Usage

Value

Arguments