locits (version 1.7.7)

AutoBestBW: Choose a good bandwidth for running mean smoothing of a EWS spectral estimator.

Description

Computes running mean estimator closest to wavelet estimator of evolutionary wavelet spectrum. The idea is to obtain a good linear bandwidth.

Usage

AutoBestBW(x, filter.number = 1, family = "DaubExPhase",
    smooth.dev = var, AutoReflect = TRUE, tol = 0.01, maxits = 200,
    plot.it = FALSE, verbose = 0, ReturnAll = FALSE)

Value

If ReturnAll argument is FALSE then the best bandwidth is returned.

Arguments

x

Time series you want to analyze.

filter.number

The wavelet filter used to carry out smoothing operations.

family

The wavelet family used to carry out smoothing operations.

smooth.dev

The deviance estimate used for the smoothing (see ewspec help)

AutoReflect

Mitigate periodic boundary conditions of wavelet transforms by reflecting time series about RHS end before taking transforms (and is undone before returning the answer).

tol

Tolerance for golden section search for the best bandwidth

maxits

Maximum number of iterations for the golden section search

plot.it

Plot the values of the bandwidth and its closeness of the linear smooth to the wavelet smooth, if TRUE.

verbose

If nonzero prints out informative messages about the progress of the golden section search. Higher integers produce more messages.

ReturnAll

If TRUE then return the best bandwidth (in the ans component), the wavelet smooth (in EWS.wavelet) and the closest linear smooth (EWS.linear). If FALSE then just the bandwidth is returned.

Author

Guy Nason.

Details

Tries to find the best running mean fit to an estimated spectrum obtained via wavelet shrinkage. The goal is to try and find a reasonable linear bandwidth.

References

Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. tools:::Rd_expr_doi("10.1111/rssb.12015")

See Also

Rvarlacf

Examples

#
# Generate synthetic data
#
x <- rnorm(256)
#
# Compute best linear bandwidth
#
tmp <- AutoBestBW(x=x)
#
# Printing it out in my example gives:
# tmp
# [1] 168