fepoisson

A wrapper for <code>feglm</code> with
<code>family = poisson()</code>.

Fast and user-friendly estimation of generalized linear models with
multiple fixed effects and cluster the standard errors. The method to obtain
the estimated fixed-effects coefficients is based on Stammann (2018)
<doi:10.48550/arXiv.1707.01815>, Gaure (2013)
<doi:10.1016/j.csda.2013.03.024>, Berge (2018)
<https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020)
<doi: 10.1177/1536867X20909691>.

Mauricio Vargas Sepulveda

capybara

Fast and Memory Efficient Fitting of Linear Models with
High-Dimensional Fixed Effects

Yoto Yotov

fepoisson function

<dl><dt>formula</dt>
<dd>an object of class <code>"formula"</code>: a symbolic description of
the model to be fitted. <code>formula</code> must be of type <code>y ~ X | k</code>,
where the second part of the formula refers to factors to be concentrated
out. It is also possible to pass clustering variables to <code>feglm</code>
as <code>y ~ X | k | c</code>.</dd>
<dt>data</dt>
<dd>an object of class <code>"data.frame"</code> containing the variables
in the model. The expected input is a dataset with the variables specified
in <code>formula</code> and a number of rows at least equal to the number of
variables in the model.</dd>
<dt>weights</dt>
<dd>an optional string with the name of the 'prior weights'
variable in <code>data</code>.</dd>
<dt>beta_start</dt>
<dd>an optional vector of starting values for the structural
parameters in the linear predictor. Default is
\(\boldsymbol{\beta} = \mathbf{0}\).</dd>
<dt>eta_start</dt>
<dd>an optional vector of starting values for the linear
predictor.</dd>
<dt>control</dt>
<dd>a named list of parameters for controlling the fitting
process. See <code>fit_control</code> for details.</dd></dl>

Arguments

Poisson model fitting high-dimensional with k-way fixed effects — fepoisson

<dl>

<dt>formula</dt>
<dd>an object of class <code>"formula"</code>: a symbolic description of
the model to be fitted. <code>formula</code> must be of type <code>y ~ X | k</code>,
where the second part of the formula refers to factors to be concentrated
out. It is also possible to pass clustering variables to <code>feglm</code>
as <code>y ~ X | k | c</code>.</dd>


<dt>data</dt>
<dd>an object of class <code>"data.frame"</code> containing the variables
in the model. The expected input is a dataset with the variables specified
in <code>formula</code> and a number of rows at least equal to the number of
variables in the model.</dd>


<dt>weights</dt>
<dd>an optional string with the name of the 'prior weights'
variable in <code>data</code>.</dd>


<dt>beta_start</dt>
<dd>an optional vector of starting values for the structural
parameters in the linear predictor. Default is
\(\boldsymbol{\beta} = \mathbf{0}\).</dd>


<dt>eta_start</dt>
<dd>an optional vector of starting values for the linear
predictor.</dd>


<dt>control</dt>
<dd>a named list of parameters for controlling the fitting
process. See <code>fit_control</code> for details.</dd>

</dl>

fepoisson: Poisson model fitting high-dimensional with k-way fixed effects

Description

Usage

Value

Arguments

Examples