# robust_HC1: Heteroskedasticity-Robust Variance-Covariance Matrix... In baruuum/jars: Just Another Robust Standard errors package

## Description

Heteroskedasticity-Robust Variance-Covariance Matrix Estimator (HC1)

## Usage

 1 robust_HC1(X, e) 

## Arguments

 X the model matrix. Can be obtained by applying the function model.matrix on a lm object. e vector of residuals. Can be obtained by applying the function resid on a lm object.

## Details

The different types of robust estimators differ in their degrees-of-freedom corrections for finite sample bias. In the case of HC1, the diagonal entries of the "meat" matrix, ω_i are given as

ω_{i}^2 = \hat{e}_{i}^2 \Bigg( \frac{n}{n - k} \Bigg)

where \hat e_i are the residuals of the model, n is the sample size, and k is the number of predictors in the model.

## Value

returns a heteroskedasticity-robust variance-covariance matrix of type HC1.

baruuum/jars documentation built on Nov. 3, 2019, 2:06 p.m.