sumtest

<code>sumtest()</code> uses the estimations across several cut-offs to test whether
the sum of the deviations between sample and population FODR differ
significantly from its expected value._k = 1^K n(_c_k - _c_k) sum k = (1 to K) sqrt(n) (gammahat_c_k - gamma_ck)

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

sumtest function

<dl><dt>robust2sls_object</dt>
<dd>A list of <code>"robust2sls"</code> objects.</dd>
<dt>alpha</dt>
<dd>A numeric value between 0 and 1 representing the significance
level of the test.</dd>
<dt>iteration</dt>
<dd>An integer &gt;= 0 or the character "convergence" that
determines which iteration is used for the test.</dd>
<dt>one_sided</dt>
<dd>A logical value whether a two-sided test (<code>FALSE</code>)
should be conducted or a one-sided test (<code>TRUE</code>) that rejects only
when the false outlier detection rate is above its expected value.</dd></dl>

Arguments

<code>sumtest()</code> uses the estimations across several cut-offs to test whether
the sum of the deviations between sample and population FODR differ
significantly from its expected value.

_k = 1^K n(_c_k - _c_k) sum k = (1 to K) sqrt(n) (gammahat_c_k - gamma_ck)

Scaling sum proportion test across different cut-offs — sumtest

<dl>

<dt>robust2sls_object</dt>
<dd>A list of <code>"robust2sls"</code> objects.</dd>


<dt>alpha</dt>
<dd>A numeric value between 0 and 1 representing the significance
level of the test.</dd>


<dt>iteration</dt>
<dd>An integer &gt;= 0 or the character "convergence" that
determines which iteration is used for the test.</dd>


<dt>one_sided</dt>
<dd>A logical value whether a two-sided test (<code>FALSE</code>)
should be conducted or a one-sided test (<code>TRUE</code>) that rejects only
when the false outlier detection rate is above its expected value.</dd>

</dl>

sumtest: Scaling sum proportion test across different cut-offs

Description

Usage

Value

Arguments