sandwich (version 2.4-0)

# NeweyWest: Newey-West HAC Covariance Matrix Estimation

## Description

A set of functions implementing the Newey & West (1987, 1994) heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators.

## Usage

```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(), …)```

## Arguments

x

a fitted model object.

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"`.

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.

## Value

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

`bwNeweyWest` returns the selected bandwidth parameter.

## Details

`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.

## References

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/.

`vcovHAC`, `weightsAndrews`, `kernHAC`

## Examples

Run this code
``````# 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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