`NeweyWest.Rd`

A set of functions implementing the Newey & West (1987, 1994) heteroscedasticity 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(), ...)
```

- x
a fitted model object. For

`bwNeweyWest`

it can also be a score matrix (as returned by`estfun`

) directly.- lag
integer specifying the maximum lag with positive weight for the Newey-West estimator. If set to

`NULL`

`floor(bwNeweyWest(x, ...))`

is used.- 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`

. The default is to use VAR(1) prewhitening.- kernel
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"`

.- 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.- ar.method
character. The

`method`

argument passed to`ar`

for prewhitening (only, not for bandwidth selection).- 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.- verbose
logical. Should the lag truncation parameter used be printed?

- weights
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`

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.

`NeweyWest`

returns the same type of object as `vcovHAC`

which is typically just the covariance matrix.
`bwNeweyWest`

returns the selected bandwidth parameter.

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.
doi:10.18637/jss.v011.i10

```
## fit investment equation
data(Investment)
fm <- lm(RealInv ~ RealGNP + RealInt, data = Investment)
## Newey & West (1994) compute this type of estimator
NeweyWest(fm)
#> (Intercept) RealGNP RealInt
#> (Intercept) 594.1004817 -0.5617817294 36.04992496
#> RealGNP -0.5617817 0.0005563172 -0.04815937
#> RealInt 36.0499250 -0.0481593694 13.24912546
## The Newey & West (1987) estimator requires specification
## of the lag and suppression of prewhitening
NeweyWest(fm, lag = 4, prewhite = FALSE)
#> (Intercept) RealGNP RealInt
#> (Intercept) 359.4170681 -0.3115505035 -4.089319305
#> RealGNP -0.3115505 0.0002805888 -0.005355931
#> RealInt -4.0893193 -0.0053559312 11.171472998
## bwNeweyWest() can also be passed to kernHAC(), e.g.
## for the quadratic spectral kernel
kernHAC(fm, bw = bwNeweyWest)
#> (Intercept) RealGNP RealInt
#> (Intercept) 794.986166 -0.7562570101 48.19485118
#> RealGNP -0.756257 0.0007537517 -0.06485461
#> RealInt 48.194851 -0.0648546058 17.58798679
```