collinearity_check

<code>collinearity_check</code> checks for perfect multicollinearity in a model with high-dimensional
fixed effects. It calls <code>lfe::demeanlist</code> in order to partial out the fixed effects, and then
uses <code><a href="/link/stats%3A%3Alm.wfit?package=penppml&version=0.2.4" data-mini-rdoc="penppml::stats::lm.wfit">stats::lm.wfit</a></code> to discard linearly dependent variables.

A set of tools that enables efficient estimation of penalized
Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models
that feature one or more sets of high-dimensional fixed effects. The methodology is based on
Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451>
and takes advantage of the method of alternating projections of Gaure (2013)
<doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as
the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010)
<doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out
cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016)
<doi:10.1080/07350015.2015.1102733>.

Joao Cruz

penppml

Penalized Poisson Pseudo Maximum Likelihood Regression

Diego Ferreras Garrucho

Tom Zylkin

Nicolas Apfel

collinearity_check function

<dl><dt>y</dt>
<dd>Dependent variable (a numeric vector).</dd>
<dt>x</dt>
<dd>Regressor matrix.</dd>
<dt>fes</dt>
<dd>List of fixed effects.</dd>
<dt>hdfetol</dt>
<dd>Tolerance for the centering, passed on to <code>lfe::demeanlist</code>.</dd>
<dt>colcheck_x_fes</dt>
<dd>Logical. If <code>TRUE</code>, this checks whether the independent variables are perfectly explained
by the fixed effects drops those that are perfectly explained.</dd>
<dt>colcheck_x</dt>
<dd>Logical. If <code>TRUE</code>, this checks collinearity between the independent variables and drops the
collinear variables.</dd></dl>

Arguments

<code>collinearity_check</code> checks for perfect multicollinearity in a model with high-dimensional
fixed effects. It calls <code>lfe::demeanlist</code> in order to partial out the fixed effects, and then
uses <code><a href='https://rdrr.io/r/stats/lmfit.html'>stats::lm.wfit</a></code> to discard linearly dependent variables.

Checking for Perfect Multicollinearity — collinearity_check

<dl>

<dt>y</dt>
<dd>Dependent variable (a numeric vector).</dd>


<dt>x</dt>
<dd>Regressor matrix.</dd>


<dt>fes</dt>
<dd>List of fixed effects.</dd>


<dt>hdfetol</dt>
<dd>Tolerance for the centering, passed on to <code>lfe::demeanlist</code>.</dd>


<dt>colcheck_x_fes</dt>
<dd>Logical. If <code>TRUE</code>, this checks whether the independent variables are perfectly explained
by the fixed effects drops those that are perfectly explained.</dd>


<dt>colcheck_x</dt>
<dd>Logical. If <code>TRUE</code>, this checks collinearity between the independent variables and drops the
collinear variables.</dd>

</dl>

collinearity_check: Checking for Perfect Multicollinearity

Description

Usage

Value

Arguments