weightsAndrews {sandwich} | R Documentation

## Kernel-based HAC Covariance Matrix Estimation

### Description

A set of functions implementing a class of kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

### Usage

    kernHAC(x, order.by = NULL, prewhite = 1, bw = bwAndrews,
      kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"),
      approx = c("AR(1)", "ARMA(1,1)"), adjust = TRUE, diagnostics = FALSE,
      sandwich = TRUE, ar.method = "ols", tol = 1e-7, data = list(), verbose = FALSE, ...)

    weightsAndrews(x, order.by = NULL, bw = bwAndrews,
      kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"),
      prewhite = 1, ar.method = "ols", tol = 1e-7, data = list(), verbose = FALSE, ...)

    bwAndrews(x, order.by = NULL, kernel = c("Quadratic Spectral", "Truncated",
      "Bartlett", "Parzen", "Tukey-Hanning"), approx = c("AR(1)", "ARMA(1,1)"),
      weights = NULL, prewhite = 1, ar.method = "ols", data = list(), ...)

### Arguments

`x` | a fitted model object.
`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.
`bw` | numeric or a function. The bandwidth of the kernel (corresponds to the truncation lag). If set to to a function (the default is `bwAndrews`) it is adaptively chosen.
`kernel` | a character specifying the kernel used. All kernels used are described in Andrews (1991).
`approx` | a character specifying the approximation method if the bandwidth `bw` has to be chosen by `bwAndrews`.
`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).
`tol` | numeric. Weights that exceed `tol` are used for computing the covariance matrix, all other weights are treated as 0.
`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 bandwidth parameter used be printed?
`...` | further arguments passed to `bwAndrews`.
`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).

### Details

`kernHAC` is a convenience interface to `vcovHAC` using `weightsAndrews`: first a weights function is defined and then `vcovHAC` is called.

The kernel weights underlying `weightsAndrews` are directly accessible via the function `kweights` and require the specification of the bandwidth parameter `bw`. If this is not specified it can be chosen adaptively by the function `bwAndrews` (except for the `"Truncated"` kernel). The automatic bandwidth selection is based on an approximation of the estimating functions by either AR(1) or ARMA(1,1) processes. To aggregate the estimated parameters from these approximations a weighted sum is used. The `weights` in this aggregation are by default all equal to 1 except that corresponding to the intercept term which is set to 0 (unless there is no other variable in the model) making the covariance matrix scale invariant.

Further details can be found in Andrews (1991).

The estimator of Newey & West (1987) is a special case of the class of estimators introduced by Andrews (1991). It can be obtained using the `"Bartlett"` kernel and setting `bw` to `lag + 1`. A convenience interface is provided in `NeweyWest`.

### Value

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

`weightsAndrews` returns a vector of weights.

`bwAndrews` 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.

### See Also

`vcovHAC`, `NeweyWest`, `weightsLumley`, `weave`

### Examples

    curve(kweights(x, kernel = "Quadratic", normalize = TRUE),
          from = 0, to = 3.2, xlab = "x", ylab = "k(x)")
    curve(kweights(x, kernel = "Bartlett", normalize = TRUE),
          from = 0, to = 3.2, col = 2, add = TRUE)
    curve(kweights(x, kernel = "Parzen", normalize = TRUE),
          from = 0, to = 3.2, col = 3, add = TRUE)
    curve(kweights(x, kernel = "Tukey", normalize = TRUE),
          from = 0, to = 3.2, col = 4, add = TRUE)
    curve(kweights(x, kernel = "Truncated", normalize = TRUE),
          from = 0, to = 3.2, col = 5, add = TRUE)

    ## fit investment equation
    data(Investment)
    fm <- lm(RealInv ~ RealGNP + RealInt, data = Investment)

    ## compute quadratic spectral kernel HAC estimator
    kernHAC(fm)
    kernHAC(fm, verbose = TRUE)

    ## use Parzen kernel instead, VAR(2) prewhitening, no finite sample
    ## adjustment and Newey & West (1994) bandwidth selection
    kernHAC(fm, kernel = "Parzen", prewhite = 2, adjust = FALSE,
      bw = bwNeweyWest, verbose = TRUE)

    ## compare with estimate under assumption of spheric errors
    vcov(fm)

[Package sandwich version 2.5-1 Index]