test_cpv

<code>test_cpv</code> returns the critical value corresponding to a given
quantile of the simulated distribution and the p-value of the test statistic.

internal

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

test_cpv function

<dl><dt>dist</dt>
<dd>A numeric vector of simulated values approximating the
distribution of the test statistic, e.g. generated as in <code>mvn_sup()</code>.</dd>
<dt>teststat</dt>
<dd>A numeric value of the test statistic.</dd>
<dt>p</dt>
<dd>A numeric vector of probabilities with values in [0,1] for which the
corresponding quantiles are calculated.</dd></dl>

Arguments

Critical and p-value for test statistic relative to simulated distribution — test_cpv

<dl>

<dt>dist</dt>
<dd>A numeric vector of simulated values approximating the
distribution of the test statistic, e.g. generated as in <code>mvn_sup()</code>.</dd>


<dt>teststat</dt>
<dd>A numeric value of the test statistic.</dd>


<dt>p</dt>
<dd>A numeric vector of probabilities with values in [0,1] for which the
corresponding quantiles are calculated.</dd>

</dl>

test_cpv: Critical and p-value for test statistic relative to simulated distribution

Description

Usage

Value

Arguments