A set of functions implementing the Newey & West (1987, 1994) heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators.
NeweyWest(x, lag = NULL, order.by = NULL, prewhite = TRUE, adjust = FALSE,
diagnostics = FALSE, sandwich = TRUE, ar.method = "ols", data = list(),
verbose = FALSE)bwNeweyWest(x, order.by = NULL, kernel = c("Bartlett", "Parzen",
"Quadratic Spectral", "Truncated", "Tukey-Hanning"), weights = NULL,
prewhite = 1, ar.method = "ols", data = list(), …)
a fitted model object.
integer specifying the maximum lag with positive
weight for the Newey-West estimator. If set to NULL
floor(bwNeweyWest(x, ...)) is used.
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).
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. The default is to
use VAR(1) prewhitening.
a character specifying the kernel used. All kernels used
are described in Andrews (1991). bwNeweyWest can only
compute bandwidths for "Bartlett", "Parzen" and
"Quadratic Spectral".
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.
logical. Should additional model diagnostics be returned?
See vcovHAC for details.
logical. Should the sandwich estimator be computed?
If set to FALSE only the middle matrix is returned.
character. The method argument passed to
ar for prewhitening (only, not for bandwidth selection).
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.
logical. Should the lag truncation parameter used be printed?
numeric. A vector of weights used for weighting the estimated
coefficients of the approximation model (as specified by approx). By
default all weights are 1 except that for the intercept term (if there is more than
one variable).
currently not used.
NeweyWest returns the same type of object as vcovHAC
which is typically just the covariance matrix.
bwNeweyWest returns the selected bandwidth parameter.
NeweyWest is a convenience interface to vcovHAC using
Bartlett kernel weights as described in Newey & West (1987, 1994).
The automatic bandwidth selection procedure described in Newey & West (1994)
is used as the default and can also be supplied to kernHAC for the
Parzen and quadratic spectral kernel. It is implemented in bwNeweyWest
which does not truncate its results - if the results for the Parzen and Bartlett
kernels should be truncated, this has to be applied afterwards. For Bartlett
weights this is implemented in NeweyWest.
To obtain the estimator described in Newey & West (1987), prewhitening has to be suppressed.
Andrews DWK (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59, 817--858.
Newey WK & West KD (1987), A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703--708.
Newey WK & West KD (1994), Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, 631--653.
Zeileis A (2004), Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), 1--17. URL http://www.jstatsoft.org/v11/i10/.
# NOT RUN {
## fit investment equation
data(Investment)
fm <- lm(RealInv ~ RealGNP + RealInt, data = Investment)
## Newey & West (1994) compute this type of estimator
NeweyWest(fm)
## The Newey & West (1987) estimator requires specification
## of the lag and suppression of prewhitening
NeweyWest(fm, lag = 4, prewhite = FALSE)
## bwNeweyWest() can also be passed to kernHAC(), e.g.
## for the quadratic spectral kernel
kernHAC(fm, bw = bwNeweyWest)
# }
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