meat

Estimating the variance of the estimating functions of
 a regression model by cross products of the empirical
 estimating functions.

regression

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic
robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

Achim Zeileis

sandwich

Robust Covariance Matrix Estimators

Thomas Lumley

Nathaniel Graham

Susanne Koell

meat function

<dl> <dt>x</dt>
<dd>a fitted model object.</dd> <dt>adjust</dt>
<dd>logical. Should a finite sample adjustment be made?
 This amounts to multiplication with \(n/(n-k)\) where \(n\) is the
 number of observations and \(k\) the number of estimated parameters.</dd> <dt>...</dt>
<dd>arguments passed to the <code>estfun</code> function.</dd></dl>

Arguments

A Simple Meat Matrix Estimator — meat

<dl>

 <dt>x</dt>
<dd>a fitted model object.</dd>

 <dt>adjust</dt>
<dd>logical. Should a finite sample adjustment be made?
 This amounts to multiplication with \(n/(n-k)\) where \(n\) is the
 number of observations and \(k\) the number of estimated parameters.</dd>

 <dt>...</dt>
<dd>arguments passed to the <code>estfun</code> function.</dd>

</dl>

meat: A Simple Meat Matrix Estimator

Description

Usage

Value

Arguments

Details

References

See Also

Examples