lrvar function

Long-Run Variance of the Mean

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

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).

Returns

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

See Also

kernHAC, NeweyWest, vcovHAC

Examples

suppressWarnings(RNGversion("3.5.0"))
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)