Heteroskedasticity and Autocorrelation Consistent (HAC) Covariance Matrix Estimation

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

vcovHAC(x, = NULL, prewhite = FALSE, weights = weightsAndrews,
  diagnostics = FALSE, sandwich = TRUE, data = list())
a fitted model object of class "lm" or "glm".
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 assum
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 = F
Either a vector of weights for the autocovariances or a function to compute these weights based on x,, prewhite and data. If weights is a function it has to take these a
logical. Should additional model diagnostics be returned? See below for details.
logical. Should the sandwich estimator be computed? If set to FALSE only the middle matrix is returned.
an optional data frame containing the variables in the 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,, 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 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. }

    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}

  • vcovHAC
Documentation reproduced from package sandwich, version 0.1-1, License: GPL version 2

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