R: Weighted Empirical Adaptive Variance Estimation

weightsLumley {sandwich}

R Documentation

Weighted Empirical Adaptive Variance Estimation

Description

A set of functions implementing a class of kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

Usage

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(), ...)

Arguments

`x`	a fitted model object.
`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"` and 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.

Details

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).

Value

weave returns the same type of object as vcovHAC which is typically just the covariance matrix.

weightsLumley returns a vector of weights.

References

Lumley A & Heagerty P (1999), Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression. Journal of the Royal Statistical Society B, 61, 459–477.

Examples

x <- sin(1:100)
y <- 1 + x + rnorm(100)
fm <- lm(y ~ x)
weave(fm)
vcov(fm)

[Package sandwich version 2.2-9 Index]