globaltest

<code>globaltest()</code> uses several proportion or count tests with different
cut-offs to test a global hypothesis of no outliers using the Simes (1986)
procedure to account for multiple testing.

An implementation of easy tools for outlier robust inference in
two-stage least squares (2SLS) models. The user specifies a reference
distribution against which observations are classified as outliers or not.
After removing the outliers, adjusted standard errors are automatically
provided. Furthermore, several statistical tests for the false outlier
detection rate can be calculated. The outlier removing algorithm can be
iterated a fixed number of times or until the procedure converges. The
algorithms and robust inference are described in more detail in Jiao (2019)
<https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Jonas Kurle

robust2sls

Outlier Robust Two-Stage Least Squares Inference and Testing

globaltest function

<dl><dt>tests</dt>
<dd>A data frame that contains a column named <code>$pval</code>
containing the different p-values for different hypothesis tests, each
stored in a row.</dd>
<dt>global_alpha</dt>
<dd>A numeric value representing the global significance
level.</dd></dl>

Arguments

Global test correcting for multiple hypothesis testing — globaltest

<dl>

<dt>tests</dt>
<dd>A data frame that contains a column named <code>$pval</code>
containing the different p-values for different hypothesis tests, each
stored in a row.</dd>


<dt>global_alpha</dt>
<dd>A numeric value representing the global significance
level.</dd>

</dl>

globaltest: Global test correcting for multiple hypothesis testing

Description

Usage

Value

Arguments

Details

See Also