vcovHAC {sandwich} | R Documentation |
Heteroskedasticity and autocorrelation consistent (HAC) estimation of the covariance matrix of the coefficient estimates in a (generalized) linear regression model.
vcovHAC(x, order.by = NULL, prewhite = FALSE, weights = weightsAndrews, adjust = TRUE, diagnostics = FALSE, sandwich = TRUE, ar.method = "ols", data = list())
x |
a fitted model object of class "lm" or "glm" . |
order.by |
Either a vector 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 assumed to be ordered (e.g., a
time series). |
prewhite |
logical or integer. Should the estimating functions
be prewhitened? If TRUE or greater than 0 a VAR model of
order as.integer(prewhite) is fitted via ar with
method "ols" and demean = FALSE . |
weights |
Either a vector of weights for the autocovariances or a
function to compute these weights based on x , order.by ,
prewhite , ar.method and data . If weights
is a function it has to take these arguments. See also details. |
adjust |
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. |
diagnostics |
logical. Should additional model diagnostics be returned? See below for details. |
sandwich |
logical. Should the sandwich estimator be computed?
If set to FALSE only the middle matrix is returned. |
ar.method |
character. The method argument passed to
ar for prewhitening. |
data |
an optional data frame containing the variables in the order.by
model. By default the variables are taken from the environment which
vcovHAC is called from. |
This function constructs weighted information sandwich variance estimators
for (generalized) linear 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.
A matrix containing the covariance matrix estimate. If diagnostics
was set to TRUE
this has an attribute "diagnostics
which is a list
with
bias.correction |
multiplicative bias correction |
df |
Approximate denominator degrees of freedom |
Andrews DWK (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59, 817–858.
Andrews DWK & Monahan JC (1992), An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimatior. Econometrica, 60, 953–966.
Lumley A & Heagerty P (1999), Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression. Journal of the Royal Statistical Society B, 61, 459–477.
Newey WK & West KD (1987), A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703–708.
Zeileis A (2004), Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), 1–17.
weightsLumley
, weightsAndrews
,
weave
, kernHAC
x <- sin(1:100) y <- 1 + x + rnorm(100) fm <- lm(y ~ x) vcovHAC(fm) vcov(fm)