nonmissing

<code>nonmissing</code> takes a dataframe and a formula and determines which
observations can principally be used for the estimation of the 2SLS model
that is specified by the formula. Observations where any of the y, x, or z
variables are missing will be set to FALSE. While technically, fitted values
and residuals could be calculated for observations where only any of the
outside instruments is missing, this is often not desirable. This would cause
the sample on which the model is estimated to be different from the sample
on which the outliers are determined.

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

nonmissing function

<dl><dt>data</dt>
<dd>A dataframe.</dd>
<dt>formula</dt>
<dd>A formula for the <code>ivreg</code> function, i.e. in
format <code>y ~ x1 + x2 | z1 + z2</code>.</dd></dl>

Arguments

Determine which observations can be used for estimation — nonmissing

<dl>

<dt>data</dt>
<dd>A dataframe.</dd>


<dt>formula</dt>
<dd>A formula for the <code>ivreg</code> function, i.e. in
format <code>y ~ x1 + x2 | z1 + z2</code>.</dd>

</dl>

nonmissing: Determine which observations can be used for estimation

Description

Usage

Value

Arguments