hccme: Heteroskedasticity-Consistent Covariance Matrix Estimators...

In skedastic: Handling Heteroskedasticity in the Linear Regression Model

Heteroskedasticity-Consistent Covariance Matrix Estimators for Linear Regression Models

Description

Computes an estimate of the n\times n covariance matrix \Omega (assumed to be diagonal) of the random error vector of a linear regression model, using a specified method

Usage

hccme(
  mainlm,
  hcnum = c("3", "0", "1", "2", "4", "5", "6", "7", "4m", "5m", "const"),
  sandwich = FALSE,
  as_matrix = TRUE
)

Arguments

mainlm	Either an object of class "lm" (e.g., generated by lm), or a list of two objects: a response vector and a design matrix. The objects are assumed to be in that order, unless they are given the names "X" and "y" to distinguish them. The design matrix passed in a list must begin with a column of ones if an intercept is to be included in the linear model. The design matrix passed in a list should not contain factors, as all columns are treated 'as is'. For tests that use ordinary least squares residuals, one can also pass a vector of residuals in the list, which should either be the third object or be named "e".
hcnum	A character corresponding to a subscript in the name of an HCCME according to the usual nomenclature \mathrm{HC\#}. Possible values are: "3", the default, corresponding to HC3 \insertCiteMacKinnon85skedastic "0", corresponding to HC0 \insertCiteWhite80skedastic "1", corresponding to HC1 \insertCiteMacKinnon85skedastic "2", corresponding to HC1 \insertCiteMacKinnon85skedastic "4", corresponding to HC4 \insertCiteCribari04skedastic "5", corresponding to HC5 \insertCiteCribari07skedastic "6", corresponding to HC6 \insertCiteAftab16skedastic "7", corresponding to HC7 \insertCiteAftab18skedastic "4m", corresponding to HC4m \insertCiteCribari11skedastic "5m", corresponding to HC5m \insertCiteLi17skedastic "const", corresponding to the homoskedastic estimator, (n-p)^{-1}\displaystyle\sum_{i=1}^{n}e_i^2
sandwich	A logical, defaulting to FALSE, indicating whether or not the sandwich estimator \mathrm{Cov}{\hat{\beta}}=(X'X)^{-1}X'\hat{\Omega}X(X'X)^{-1} should be returned instead of \mathrm{Cov}(\epsilon)=\hat{\Omega}
as_matrix	A logical, defaulting to TRUE, indicating whether a covariance matrix estimate should be returned rather than a vector of variance estimates

Value

A numeric matrix (if as_matrix is TRUE) or else a numeric vector

References

\insertAllCited

See Also

vcovHC

Examples

mtcars_lm <- lm(mpg ~ wt + qsec + am, data = mtcars)
Omega_hat <- hccme(mtcars_lm, hcnum = "4")
Cov_beta_hat <- hccme(mtcars_lm, hcnum = "4", sandwich = TRUE)

skedastic documentation built on May 29, 2024, 12:20 p.m.