datasets

Implementation of selected high-dimensional statistical and
econometric methods for estimation and inference. Efficient estimators and
uniformly valid confidence intervals for various low-dimensional causal/
structural parameters are provided which appear in high-dimensional
approximately sparse models. Including functions for fitting heteroscedastic
robust Lasso regressions with non-Gaussian errors and for instrumental variable
(IV) and treatment effect estimation in a high-dimensional setting. Moreover,
the methods enable valid post-selection inference and rely on a theoretically
grounded, data-driven choice of the penalty.
Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.

Martin Spindler

High-Dimensional Metrics

Victor Chernozhukov

Christian Hansen

Philipp Bach

AJR function

<dl>
<dt>Mort</dt>
<dd>Settler mortality</dd><dt>logMort</dt>
<dd>logarithm of Mort</dd><dt>Latitude</dt>
<dd>Latitude</dd><dt>Latitude2</dt>
<dd>Latitude^2</dd><dt>Africa</dt>
<dd>Africa</dd><dt>Asia</dt>
<dd>Asia</dd><dt>Namer</dt>
<dd>North America</dd><dt>Samer</dt>
<dd>South America</dd><dt>Neo</dt>
<dd>Neo-Europes</dd><dt>GDP</dt>
<dd>GDP</dd><dt>Exprop</dt>
<dd>Average protection against expropriation risk</dd>
 </dl>

Format

AJR data set — AJR

<dl>
<dt>Mort</dt>
<dd>Settler mortality</dd>

<dt>logMort</dt>
<dd>logarithm of Mort</dd>

<dt>Latitude</dt>
<dd>Latitude</dd>

<dt>Latitude2</dt>
<dd>Latitude^2</dd>

<dt>Africa</dt>
<dd>Africa</dd>

<dt>Asia</dt>
<dd>Asia</dd>

<dt>Namer</dt>
<dd>North America</dd>

<dt>Samer</dt>
<dd>South America</dd>

<dt>Neo</dt>
<dd>Neo-Europes</dd>

<dt>GDP</dt>
<dd>GDP</dd>

<dt>Exprop</dt>
<dd>Average protection against expropriation risk</dd>
 

</dl>

AJR: AJR data set

Description

Arguments

Format

Details

References

Examples