covHC {strucchange} | R Documentation |
Heteroskedasticity-consistent estimation of the covariance matrix of the coefficient estimates in a linear regression model.
covHC(formula, type = c("HC2", "const", "HC", "HC1", "HC3"), data=list())
formula |
a symbolic description for the model to be fitted. |
type |
a character string specifying the estimation type. For details see below. |
data |
an optional data frame containing the variables in the model.
By default the variables are taken from the environment which covHC
is called from. |
When type = "const"
constant variances are assumed and
and covHC
gives the usual estimate of the covariance matrix of
the coefficient estimates:
sigma^2 (X'X)^{-1}
All other methods do not assume constant variances and are suitable in case of
heteroskedasticity. "HC"
gives White's estimator; for details see the
references.
A matrix containing the covariance matrix estimate.
MacKinnon J. G., White H. (1985), Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics 29, 305-325
## generate linear regression relationship ## with homoskedastic variances x <- sin(1:100) y <- 1 + x + rnorm(100) ## compute usual covariance matrix of coefficient estimates covHC(y~x, type="const") sigma2 <- sum(residuals(lm(y~x))^2)/98 sigma2 * solve.crossprod(cbind(1,x))