sospecden: Second order spectral density estimation using an infinite...

In iosmooth: Functions for Smoothing with Infinite Order Flat-Top Kernels

Description

Calculates a spectral density estimator using Parzen's piecewise cubic lag window, with plug-in bandwidth chosen using an infinite order pilot.

Usage

sospecden(x, l, kernel = c("Trap", "Rect", "SupSm"), x.points = seq(-pi, pi, len = 200))

Arguments

x	A univariate time series.
l	The smoothing parameter used for the infinite order pilot estimate. If missing, adaptive bandwidth choice is used via bwadap.ts.
kernel	The flat-top kernel used for the pilot estimate. Three kernels are implemented, described by the shape of their Fourier transforms. "Trap" is trapezoid shaped and is the default. The rectangular kernel is not recommended and is here for comparison only. SupSm is infinitely differentiable in the Fourier domain.
x.points	Points at which the spectral density is estimated. If x.points is set to NULL, sospecden will return a function which interpolates between the estimated points.

Value

If x.points is not NULL, the function returns a list of length 2

x	The x values at which the spectral density is estimated (x.points if specified).
y	The estimated spectral density function at the associated x values.

If x.points is NULL, the function returns the estimated spectral density function rather than its values.

Author(s)

Timothy L. McMurry

References

Politis, D. N., & Romano, J. P. (1995). Bias-corrected nonparametric spectral estimation . Journal of Time Series Analysis, 16(1), 67-103.

Politis, D. N. (2003). Adaptive bandwidth choice. Journal of Nonparametric Statistics, 15(4-5), 517-533.

See Also

bwadap.ts, bwplot.ts

Examples

x <- arima.sim(list(ar=.7, ma=-.3), 100)
bwplot(x)
plot(sospecden(x), type="l")

iosmooth documentation built on May 1, 2019, 10:30 p.m.