nonparametric_resampling

Nonparametric resampling from a data frame

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

nonparametric_resampling function

<dl><dt>df</dt>
<dd>Data frame containing observations to be sampled from.</dd>
<dt>resample</dt>
<dd>A vector of indices that extract the observations from the
data frame.</dd></dl>

Arguments

Nonparametric resampling from a data frame — nonparametric_resampling

<dl>

<dt>df</dt>
<dd>Data frame containing observations to be sampled from.</dd>


<dt>resample</dt>
<dd>A vector of indices that extract the observations from the
data frame.</dd>

</dl>

nonparametric_resampling: Nonparametric resampling from a data frame

Description

Usage

Value

Arguments

Details