`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. |
---|---|

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

order.by | Either a vector |

prewhite | logical or integer. Should the estimating functions
be prewhitened? If |

kernel | a character specifying the kernel used. All kernels used
are described in Andrews (1991). |

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 |

sandwich | logical. Should the sandwich estimator be computed?
If set to |

ar.method | character. The |

data | an optional data frame containing the variables in the |

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 |

... | 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