NeweyWest {sandwich} | R Documentation

## 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"`.
`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.

### 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.

### Value

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

`bwNeweyWest` returns the selected bandwidth parameter.

### 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/.

### See Also

`vcovHAC`, `weightsAndrews`, `kernHAC`

### Examples

    ## 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)

[Package sandwich version 2.5-1 Index]