View source: R/spectral.density.R
spectral.density | R Documentation |
Estimates the spectral density and cross spectral density of vector time series.
spectral.density( X, Y = X, freq = (-1000:1000/1000) * pi, q = max(1, floor(dim(X)[1]^(1/3))), weights = c("Bartlett", "trunc", "Tukey", "Parzen", "Bohman", "Daniell", "ParzenCogburnDavis") )
X |
a vector or a vector time series given in matrix form. Each row corresponds to a timepoint. |
Y |
a vector or vector time series given in matrix form. Each row corresponds to a timepoint. |
freq |
a vector containing frequencies in [-π, π] on which the spectral density should be evaluated. |
q |
window size for the kernel estimator, i.e. a positive integer. |
weights |
kernel used in the spectral smoothing. By default the Bartlett kernel is chosen. |
Let [X_1,…, X_T]^\prime be a T\times d_1 matrix and [Y_1,…, Y_T]^\prime be a T\times d_2 matrix. We stack the vectors and assume that (X_t^\prime,Y_t^\prime)^\prime is a stationary multivariate time series of dimension d_1+d_2. The cross-spectral density between the two time series (X_t) and (Y_t) is defined as
∑_{h\in\mathbf{Z}} \mathrm{Cov}(X_h,Y_0) e^{-ihω}.
The function spectral.density
determines the empirical cross-spectral density between the two time series (X_t) and (Y_t). The estimator is of form
\widehat{\mathcal{F}}^{XY}(ω)=∑_{|h|≤q q} w(|k|/q)\widehat{C}^{XY}(h)e^{-ihω},
with \widehat{C}^{XY}(h) defined in cov.structure
Here w is a kernel of the specified type and q is the window size. By default the Bartlett kernel w(x)=1-|x| is used.
See, e.g., Chapter 10 and 11 in Brockwell and Davis (1991) for details.
Returns an object of class freqdom
. The list is containing the following components:
operators
\quad an array. The k-th matrix in this array corresponds to the spectral density matrix evaluated at the k-th frequency listed in freq
.
freq
\quad returns argument vector freq
.
Peter J. Brockwell and Richard A. Davis Time Series: Theory and Methods Springer Series in Statistics, 2009
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