kweights: Kernel Weights

In sandwich: Robust Covariance Matrix Estimators

Kernel Weights

Description

Kernel weights for kernel-based heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

Usage

kweights(x, kernel = c("Truncated", "Bartlett", "Parzen",
  "Tukey-Hanning", "Quadratic Spectral"), normalize = FALSE)

Arguments

x	numeric.
kernel	a character specifying the kernel used. All kernels used are described in Andrews (1991).
normalize	logical. If set to TRUE the kernels are normalized as described in Andrews (1991).

Value

Value of the kernel function at x.

References

Andrews DWK (1991). “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.” Econometrica, 59, 817–858.

See Also

kernHAC, weightsAndrews

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)

sandwich documentation built on Sept. 16, 2024, 3:08 a.m.