| weightsLumley {sandwich} | R Documentation |
A set of functions implementing a class of kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).
weave(x, order.by = NULL, prewhite = FALSE, C = NULL,
method = c("truncate", "smooth"), acf = isoacf, adjust = FALSE,
diagnostics = FALSE, sandwich = TRUE, tol = 1e-7, data = list(), ...)
weightsLumley(x, order.by = NULL, C = NULL,
method = c("truncate", "smooth"), acf = isoacf, tol = 1e-7, 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. |
C |
numeric. The cutoff constant C is by default
4 for method "truncate" und 1 for method "smooth". |
method |
a character specifying the method used, see details. |
acf |
a function that computes the autocorrelation function of
a vector, by default isoacf is used. |
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 vcovHAC for details. |
sandwich |
logical. Should the sandwich estimator be computed?
If set to FALSE only the middle matrix is returned. |
tol |
numeric. Weights that exceed tol are used for computing
the covariance matrix, all other weights are treated as 0. |
data |
an optional data frame containing the variables in the order.by
model. By default the variables are taken from the environment which
the function is called from. |
... |
currently not used. |
weave is a convenience interface to vcovHAC using
weightsLumley: first a weights function is defined and then vcovHAC
is called.
Both weighting methods are based on some estimate of the autocorrelation
function r (as computed by acf) of the residuals of
the model x. The weights for the "truncate" method are
I{n * r ** 2 > C}
and the weights for the "smooth" method are
min{1, C * n * r ** 2}
where n is the number of observations in the model an C is the truncation
constant C.
Further details can be found in Lumley & Heagerty (1999).
weave returns the same type of object as vcovHAC
which is typically just the covariance matrix.
weightsLumley returns a vector of weights.
Lumley A & Heagerty P (1999), Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression. Journal of the Royal Statistical Society B, 61, 459–477.
vcovHAC, weightsAndrews,
kernHAC
x <- sin(1:100) y <- 1 + x + rnorm(100) fm <- lm(y ~ x) weave(fm) vcov(fm)