lacf: Compute localized autocovariance.
In locits: Test of Stationarity and Localized Autocovariance

lacf	R Documentation

Compute localized autocovariance.

Description

Compute localized autocovariance function for nonstationary time series. Note: this function is borrowed from the costat package, and modified to have linear smoothing, and when that package is complete, it will be removed from this package.

Usage

lacf(x, filter.number = 10, family = "DaubLeAsymm", smooth.dev = var,
    AutoReflect = TRUE, lag.max = NULL, WPsmooth.type = "RM",
    binwidth, tol=0.1, maxits=5, ABBverbose=0, verbose=FALSE, ...)

Arguments

`x`	The time series you wish to analyze
`filter.number`	Wavelet filter number you wish to use to analyse the time series (to form the wavelet periodogram, etc) See `filter.select` for more details.
`family`	Wavelet family to use, see `filter.select` for more details.
`smooth.dev`	Change variance estimate for smoothing. Note: `var` is good for this purpose.
`AutoReflect`	If `TRUE` then an internal reflection method is used to repackage the time series so that it can be analyzed by the periodic-assuming wavelet transforms.
`lag.max`	The maximum lag of acf required. If NULL then the same default as in the regular `acf` function is used.
`WPsmooth.type`	The type of smoothing used to produce the estimate. See `ewspec3` for more advice on this.
`binwidth`	If necessary, the `binwidth` for the spectral smoothing, see `ewspec3` for more info. If `WTsmooth.type=="RM"` then this argument specifies the binwidth of the kernel smoother applied to the wavelet periodogram. If the argument is missing or zero then an automatic bandwidth is calculated by `AutoBestBW`.
`tol`	Tolerance argument for `AutoBestBW`
`maxits`	Maximum iterations argument for `AutoBestBW`
`ABBverbose`	Verbosity of execution of `AutoBestBW`
`verbose`	If `TRUE` then informative message is printed
`...`	Other arguments for `ewspec3`.

Details

In essence, this routine is fairly simple. First, the EWS of the time series is computed. Then formula (14) from Nason, von Sachs and Kroisandr (2000) is applied to obtain the time-localized autocovariance from the spectral estimate.

Value

An object of class lacf which contains the autocovariance. This object can be handled by functions from the costat package. The idea in this package is that the function gets used internally and much of the same functionality can be achieved by running Rvarlacf and plot.lacfCI. However, running lacf on its own is much faster than Rvarlacf as the CI computation is intenstive.

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")}

Nason, G.P., von Sachs, R. and Kroisandt, G. (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J. R. Statist. Soc. Ser B, 62, 271-292.

Examples

#
# With wavethresh attached, note binwidth is fabricated here,
# just to make the example work. The lacf implementation in
# the costat package performs wavelet (ie maybe better) smoothing automatically
#
v <- lacf(rnorm(256), binwidth=40)
#
# With costat attached also
#
## Not run: plot(v)

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