vcovHAC(x, order.by = NULL, prewhite = FALSE, weights = weightsAndrews,
adjust = TRUE, diagnostics = FALSE, sandwich = TRUE, ar.method = "ols",
data = list(), ...)meatHAC(x, order.by = NULL, prewhite = FALSE, weights = weightsAndrews,
adjust = TRUE, diagnostics = FALSE, ar.method = "ols", data = list())
"lm" or "glm".z or a formula with a single explanatory
variable like ~ z. The observations in the model
are ordered by the size of z. If set to NULL (the
default) the observations are assumTRUE or greater than 0 a VAR model of
order as.integer(prewhite) is fitted via ar with
method "ols" and demean = Fx, order.by,
prewhite, ar.method and data. If weights
is a functioFALSE only the meat matrix is returned.method argument passed to
ar for prewhitening.order.by
model. By default the variables are taken from the environment which
vcovHAC is called from.sandwich.diagnostics
was set to TRUE this has an attribute "diagnostics} which is a list
with
item{bias.correction}{multiplicative bias correction}
item{df}{Approximate denominator degrees of freedom}
}
references{
Andrews DWK (1991),
Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.
emph{Econometrica}, bold{59}, 817--858.
Andrews DWK & Monahan JC (1992),
An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimatior.
emph{Econometrica}, bold{60}, 953--966.
Lumley A & Heagerty P (1999),
Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression.
emph{Journal of the Royal Statistical Society B}, bold{61}, 459--477.
Newey WK & West KD (1987),
A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.
emph{Econometrica}, bold{55}, 703--708.
Zeileis A (2004),
Econometric Computing with HC and HAC Covariance Matrix Estimators.
emph{Journal of Statistical Software}, bold{11}(10), 1--17.
URL url{http://www.jstatsoft.org/v11/i10/}.
Zeileis A (2006),
Object-oriented Computation of Sandwich Estimators.
emph{Journal of Statistical Software}, bold{16}(9), 1--16.
URL url{http://www.jstatsoft.org/v16/i09/}.
}
seealso{code{weightsLumley}, code{weightsAndrews},
code{weave}, code{kernHAC}}
examples{
x <- sin(1:100)
y <- 1 + x + rnorm(100)
fm <- lm(y ~ x)
vcovHAC(fm)
vcov(fm)
}
keyword{regression}
keyword{ts}
meatHAC is the real work horse for estimating
the meat of HAC sandwich estimators -- vcovHAC is a wrapper calling
sandwich and bread. See Zeileis (2006) for
more implementation details. The theoretical background, exemplified
for the linear regression model, is described in Zeileis (2004).Both functions construct weighted information sandwich variance estimators
for parametric models fitted to time series data. These are basically
constructed from weighted sums of autocovariances of the estimation functions
(as extracted by estfun). The crucial step is the specification
of weights: the user can either supply vcovHAC with some vector of
weights or with a function that computes these weights adaptively (based on
the arguments x, order.by, prewhite and data).
Two functions for adaptively choosing weights are implemented in
weightsAndrews implementing the results of Andrews (1991) and
in weightsLumley implementing the results of Lumley (1999).
The functions kernHAC and weave respectively
are to more convenient interfaces for vcovHAC with these functions.
Prewhitening based on VAR approximations is described as suggested in Andrews & Monahan (1992).
The covariance matrix estimators have been improved by the addition of a bias correction and an approximate denominator degrees of freedom for test and confidence interval construction.