summary.feglm

Summary statistics for objects of class <code>"feglm"</code>.

Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.

Amrei Stammann

alpaca

Fit GLM's with High-Dimensional k-Way Fixed Effects

Daniel Czarnowske

summary.feglm function

<dl><dt>object</dt>
<dd>an object of class <code>"feglm"</code>.</dd>
<dt>type</dt>
<dd>the type of covariance estimate required. <code>"hessian"</code> refers to the inverse
of the negative expected Hessian after convergence and is the default option. 
<code>"outer.product"</code> is the outer-product-of-the-gradient estimator, 
<code>"sandwich"</code> is the sandwich estimator (sometimes also refered as robust estimator), 
and <code>"clustered"</code> computes a clustered covariance matrix given some cluster variables.</dd>
<dt>cluster</dt>
<dd>a symbolic description indicating the clustering of observations.</dd>
<dt>cluster.vars</dt>
<dd>deprecated; use <code>cluster</code> instead.</dd>
<dt>...</dt>
<dd>other arguments.</dd></dl>

Arguments

Summarizing models of class feglm — summary.feglm

<dl>

<dt>object</dt>
<dd>an object of class <code>"feglm"</code>.</dd>


<dt>type</dt>
<dd>the type of covariance estimate required. <code>"hessian"</code> refers to the inverse
of the negative expected Hessian after convergence and is the default option. 
<code>"outer.product"</code> is the outer-product-of-the-gradient estimator, 
<code>"sandwich"</code> is the sandwich estimator (sometimes also refered as robust estimator), 
and <code>"clustered"</code> computes a clustered covariance matrix given some cluster variables.</dd>


<dt>cluster</dt>
<dd>a symbolic description indicating the clustering of observations.</dd>


<dt>cluster.vars</dt>
<dd>deprecated; use <code>cluster</code> instead.</dd>


<dt>...</dt>
<dd>other arguments.</dd>

</dl>

summary.feglm: Summarizing models of class `feglm`

Description

Usage

Value

Arguments

Details

References

See Also