mvn_sup

<code>mvn_sup</code> simulates the distribution of the supremum of the specified
multivariate normal distribution by drawing repeatedly from the multivariate
normal distribution and calculating the maximum of each vector.

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

mvn_sup function

<dl><dt>n</dt>
<dd>An integer determining the number of draws from the multivariate
normal distribution.</dd>
<dt>mu</dt>
<dd>A numeric vector representing the mean of the multivariate normal
distribution.</dd>
<dt>Sigma</dt>
<dd>A numeric matrix representing the variance-covariance matrix of
the mutlivariate normal distribution.</dd>
<dt>seed</dt>
<dd>An integer setting the random seed or <code>NULL</code> if it should
not be set.</dd></dl>

Arguments

Multivariate normal supremum simulation — mvn_sup

<dl>

<dt>n</dt>
<dd>An integer determining the number of draws from the multivariate
normal distribution.</dd>


<dt>mu</dt>
<dd>A numeric vector representing the mean of the multivariate normal
distribution.</dd>


<dt>Sigma</dt>
<dd>A numeric matrix representing the variance-covariance matrix of
the mutlivariate normal distribution.</dd>


<dt>seed</dt>
<dd>An integer setting the random seed or <code>NULL</code> if it should
not be set.</dd>

</dl>

mvn_sup: Multivariate normal supremum simulation

Description

Usage

Value

Arguments