AutoBestBW: Choose a good bandwidth for running mean smoothing of a EWS...
In locits: Test of Stationarity and Localized Autocovariance

AutoBestBW

R Documentation

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)

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.

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.

Value

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

Author(s)

Guy Nason.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}

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

locits documentation built on Sept. 8, 2023, 5:07 p.m.