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).

Value

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

See also

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)
#> [1] 0.007958048

## analogous multivariate case with Newey-West estimator (without prewhitening)
y <- matrix(rnorm(200), ncol = 2)
lrvar(y, type = "Newey-West", prewhite = FALSE)
#>              [,1]         [,2]
#> [1,] 0.0097884718 0.0005978738
#> [2,] 0.0005978738 0.0073428222

## AR(1) series with autocorrelation 0.9
z <- filter(rnorm(100), 0.9, method = "recursive")
lrvar(z)
#> [1] 0.4385546