sospecden {iosmooth} | R Documentation |
Second order spectral density estimation using an infinite order pilot
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 |
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 |
Value
If x.points
is not NULL, the function returns a list of length 2
x |
The |
y |
The estimated spectral density function at the associated |
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
Examples
x <- arima.sim(list(ar=.7, ma=-.3), 100)
bwplot(x)
plot(sospecden(x), type="l")