R: Long-Run Variance of the Mean

lrvar {sandwich}

R Documentation

Long-Run Variance of the Mean

Description

Convenience function for computing the long-run variance (matrix) of a (possibly multivariate) series of observations.

Usage

lrvar(x, type = c("Andrews", "Newey-West"), prewhite = TRUE, adjust = TRUE, ...)

Arguments

`x`	numeric vector, matrix, or time series.
`type`	character specifying the type of estimator, i.e., whether `kernHAC` for the Andrews quadratic spectral kernel HAC estimator is used or `NeweyWest` for the Newey-West Bartlett HAC estimator.
`prewhite`	logical or integer. Should the series be prewhitened? Passed to `kernHAC` or `NeweyWest`.
`adjust`	logical. Should a finite sample adjustment be made? Passed to `kernHAC` or `NeweyWest`.
`...`	further arguments passed on to `kernHAC` or `NeweyWest`.

Details

lrvar is a simple wrapper function for computing the long-run variance (matrix) of a (possibly multivariate) series x. First, this simply fits a linear regression model x ~ 1 by lm. Second, the corresponding variance of the mean(s) is estimated either by kernHAC (Andrews quadratic spectral kernel HAC estimator) or by NeweyWest (Newey-West Bartlett HAC estimator).

Value

For a univariate series x a scalar variance is computed. For a multivariate series x the covariance matrix is computed.

Examples

set.seed(1)
## iid series (with variance of mean 1/n)
## and Andrews kernel HAC (with prewhitening)
x <- rnorm(100)
lrvar(x)

## analogous multivariate case with Newey-West estimator (without prewhitening)
y <- matrix(rnorm(200), ncol = 2)
lrvar(y, type = "Newey-West", prewhite = FALSE)

## AR(1) series with autocorrelation 0.9
z <- filter(rnorm(100), 0.9, method = "recursive")
lrvar(z)

[Package sandwich version 2.2-9 Index]