generate_data

<code>generate_data</code> draws random data for a 2SLS model given the parameters.

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

generate_data function

<dl><dt>parameters</dt>
<dd>A list with 2SLS model parameters as created by
generate_param.</dd>
<dt>n</dt>
<dd>Sample size to be drawn.</dd></dl>

Arguments

Random data of 2SLS model (Monte Carlo) — generate_data

<dl>

<dt>parameters</dt>
<dd>A list with 2SLS model parameters as created by
generate_param.</dd>


<dt>n</dt>
<dd>Sample size to be drawn.</dd>

</dl>

generate_data: Random data of 2SLS model (Monte Carlo)

Description

Usage

Value

Arguments