genmodel

<code>genmodel</code> transforms a data frame into the needed components for our main functions (a y vector,
a x matrix and a fes list).

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

genmodel function

<dl><dt>data</dt>
<dd>A data frame containing all relevant variables.</dd>
<dt>dep</dt>
<dd>A string with the name of the independent variable or a column number.</dd>
<dt>indep</dt>
<dd>A vector with the names or column numbers of the regressors. If left unspecified,
all remaining variables (excluding fixed effects) are included in the regressor matrix.</dd>
<dt>fixed</dt>
<dd>A vector with the names or column numbers of factor variables identifying the fixed effects,
or a list with the desired interactions between variables in <code>data</code>.</dd>
<dt>cluster</dt>
<dd>Optional. A string with the name of the clustering variable or a column number.
It's also possible to input a vector with several variables, in which case the interaction of
all of them is taken as the clustering variable.</dd>
<dt>selectobs</dt>
<dd>Optional. A vector indicating which observations to use.</dd></dl>

Arguments

Generating Model Structure — genmodel

<dl>

<dt>data</dt>
<dd>A data frame containing all relevant variables.</dd>


<dt>dep</dt>
<dd>A string with the name of the independent variable or a column number.</dd>


<dt>indep</dt>
<dd>A vector with the names or column numbers of the regressors. If left unspecified,
all remaining variables (excluding fixed effects) are included in the regressor matrix.</dd>


<dt>fixed</dt>
<dd>A vector with the names or column numbers of factor variables identifying the fixed effects,
or a list with the desired interactions between variables in <code>data</code>.</dd>


<dt>cluster</dt>
<dd>Optional. A string with the name of the clustering variable or a column number.
It's also possible to input a vector with several variables, in which case the interaction of
all of them is taken as the clustering variable.</dd>


<dt>selectobs</dt>
<dd>Optional. A vector indicating which observations to use.</dd>

</dl>

genmodel: Generating Model Structure

Description

Usage

Value

Arguments