multi_cutoff

<code>multi_cutoff()</code> runs several outlier detection algorithms that differ
in the value of the cut-off that determines whether an observation is
classified as an outlier or not.

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

multi_cutoff function

<dl><dt>gamma</dt>
<dd>A numeric vector representing the probability of falsely
classifying an observation as an outlier. One setting of the algorithm per
element of <code>gamma</code> is being run.</dd>
<dt>...</dt>
<dd>Arguments for specifying the other settings of the outlier
detection algorithm, <code>outlier_detection</code>.</dd></dl>

Arguments

Multiple models, varying cut-off — multi_cutoff

<dl>

<dt>gamma</dt>
<dd>A numeric vector representing the probability of falsely
classifying an observation as an outlier. One setting of the algorithm per
element of <code>gamma</code> is being run.</dd>


<dt>...</dt>
<dd>Arguments for specifying the other settings of the outlier
detection algorithm, <code>outlier_detection</code>.</dd>

</dl>

multi_cutoff: Multiple models, varying cut-off

Description

Usage

Value

Arguments

Details