sandwich (version 3.1-2)

vcovHAC: Heteroscedasticity and Autocorrelation Consistent (HAC) Covariance Matrix Estimation

Description

Heteroscedasticity and autocorrelation consistent (HAC) estimation of the covariance matrix of the coefficient estimates in a (generalized) linear regression model.

Usage

vcovHAC(x, ...)
# S3 method for default
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(), ...)

Value

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

Details

The function meatHAC is the real work horse for estimating the meat of HAC sandwich estimators -- the default vcovHAC method 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 estimating 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. See Lumley & Heagerty (1999) for details.

References

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 Estimator.” Econometrica, 60, 953--966.

Lumley T & 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. tools:::Rd_expr_doi("10.18637/jss.v011.i10")

Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators.” Journal of Statistical Software, 16(9), 1--16. tools:::Rd_expr_doi("10.18637/jss.v016.i09")

See Also

weightsLumley, weightsAndrews, weave, kernHAC

Examples

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