lambdaCalculation

list with options for the calculation of the penalty.<ul>
<li><code>c</code> and <code>gamma</code> constants for the penalty with default <code>c=1.1</code> and <code>gamma=0.1</code></li>
<li><code>homoscedastic</code> logical, if homoscedastic errors are considered (default <code>FALSE</code>). Option <code>none</code> is described below.</li>
<li><code>X.dependent.lambda</code> if <code>independent</code> or <code>dependent</code> design matrix <code>X</code> is assumed for calculation of the parameter \(\lambda\)</li>
<li><code>numSim</code> number of simulations for the X-dependent methods</li>
<li><code>lambda.start</code> initial penalization value, compulsory for method "none"</li>
</ul>

penalty

residual which is used for calculation of the variance or the data-dependent loadings

This function implements different methods for calculation of the penalization parameter \(\lambda\). Further details can be found under rlasso.

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

lambdaCalculation function

<dl><dt>penalty</dt>
<dd>list with options for the calculation of the penalty.<ul>
<li><code>c</code> and <code>gamma</code> constants for the penalty with default <code>c=1.1</code> and <code>gamma=0.1</code></li>
<li><code>homoscedastic</code> logical, if homoscedastic errors are considered (default <code>FALSE</code>). Option <code>none</code> is described below.</li>
<li><code>X.dependent.lambda</code> if <code>independent</code> or <code>dependent</code> design matrix <code>X</code> is assumed for calculation of the parameter \(\lambda\)</li>
<li><code>numSim</code> number of simulations for the X-dependent methods</li>
<li><code>lambda.start</code> initial penalization value, compulsory for method "none"</li>
</ul></dd>
<dt>y</dt>
<dd>residual which is used for calculation of the variance or the data-dependent loadings</dd>
<dt>x</dt>
<dd>matrix of regressor variables</dd></dl>

Arguments

Function for Calculation of the penalty parameter — lambdaCalculation

<dl>

<dt>penalty</dt>
<dd>list with options for the calculation of the penalty.<ul>
<li><code>c</code> and <code>gamma</code> constants for the penalty with default <code>c=1.1</code> and <code>gamma=0.1</code></li>
<li><code>homoscedastic</code> logical, if homoscedastic errors are considered (default <code>FALSE</code>). Option <code>none</code> is described below.</li>
<li><code>X.dependent.lambda</code> if <code>independent</code> or <code>dependent</code> design matrix <code>X</code> is assumed for calculation of the parameter \(\lambda\)</li>
<li><code>numSim</code> number of simulations for the X-dependent methods</li>
<li><code>lambda.start</code> initial penalization value, compulsory for method "none"</li>
</ul></dd>


<dt>y</dt>
<dd>residual which is used for calculation of the variance or the data-dependent loadings</dd>


<dt>x</dt>
<dd>matrix of regressor variables</dd>

</dl>

lambdaCalculation: Function for Calculation of the penalty parameter

Description

Usage

Value

Arguments