pdPgram2D: Multitaper HPD time-varying periodogram matrix
In pdSpecEst: An Analysis Toolbox for Hermitian Positive Definite Matrices

Description Usage Arguments Details Value References See Also Examples

Given a multivariate time series, pdPgram2D computes a multitapered HPD time-varying periodogram matrix based on averaging raw Hermitian PSD time-varying periodogram matrices of tapered multivariate time series segments.

1 2	pdPgram2D(X, B, tf.grid, method = c("dpss", "hermite"), nw = 3, bias.corr = F)

`X`	an (n,d)-dimensional matrix corresponding to a multivariate time series, with the `d` columns corresponding to the components of the time series.
`B`	depending on the argument `method`, either the number of orthogonal DPSS or Hermite tapering functions. By default, `B = d`, such that the multitaper periodogram is guaranteed to be positive definite.
`tf.grid`	a list with two components `tf.grid$time` and `tf.grid$frequency` specifying the rectangular grid of time-frequency points at which the multitaper periodogram is evaluated. `tf.grid$time` should be a numeric vector of rescaled time points in the range `(0,1)`. `tf.grid$frequency` should be a numeric vector of frequency points in the range `(0,0.5)`, with 0.5 corresponding to the Nyquist frequency.
`method`	the tapering method, either `"dpss"` or `"hermite"` explained in the Details section below. Defaults to `method = "dpss"`.
`nw`	a positive numeric value corresponding to the time-bandwidth parameter of the tapering functions, see also `dpss`, defaults to `nw = 3`. Both the DPSS and Hermite tapers are rescaled with the same time-bandwidth parameter.
`bias.corr`	should an asymptotic bias-correction under the affine-invariant Riemannian metric be applied to the HPD periodogram matrix? Defaults to `FALSE`.

If method = "dpss", pdPgram2D calculates a (d,d)-dimensional multitaper time-varying periodogram matrix based on sliding B DPSS (Discrete Prolate Spheroidal Sequence or Slepian) orthogonal tapering functions as in dpss applied to the d-dimensional time series X. If B ≥ d, the multitaper time-varying periodogram matrix is guaranteed to be positive definite at each time-frequency point in the grid expand.grid(tf.grid$time, tf.grid$frequency). In short, the function pdPgram2D computes a multitaper periodogram matrix (as in pdPgram) in each of a number of non-overlapping time series segments of X, with the time series segments centered around the (rescaled) time points in tf.grid$time. If method = "hermite", the function calculates a multitaper time-varying periodogram matrix replacing the DPSS tapers by orthogonal Hermite tapering functions as in e.g., \insertCiteBB96pdSpecEst.
In the case of subsequent periodogram matrix denoising in the space of HPD matrices equipped with the affine-invariant Riemannian metric, one should set bias.corr = T, thereby correcting for the asymptotic bias of the periodogram matrix in the manifold of HPD matrices equipped with the affine-invariant metric as explained in \insertCiteCvS17pdSpecEst and Chapter 3 and 5 of \insertCiteC18pdSpecEst. The pre-smoothed HPD periodogram matrix (i.e., an initial noisy HPD spectral estimator) can be given as input to the function pdSpecEst2D to perform intrinsic wavelet-based time-varying spectral matrix estimation. In this case, set bias.corr = F (the default) as the appropriate bias-corrections are applied internally by the function pdSpecEst2D.

A list containing two components:

`tf.grid`	a list with two components corresponding to the rectangular grid of time-frequency points at which the multitaper periodogram is evaluated.
`P`	a (d,d,m_1,m_2)-dimensional array with `m_1 = length(tf.grid$time)` and `m_2 = length(tf.grid$frequency)` corresponding to the (d,d)-dimensional tapered periodogram matrices evaluated at the time-frequency points in `tf.grid`.

\insertAllCited

pdPgram, dpss

## Coefficient matrices
Phi1 <- array(c(0.4, 0, 0, 0.8, rep(0, 4)), dim = c(2, 2, 2))
Phi2 <- array(c(0.8, 0, 0, 0.4, rep(0, 4)), dim = c(2, 2, 2))
Theta <- array(c(0.5, -0.7, 0.6, 0.8, rep(0, 4)), dim = c(2, 2, 2))
Sigma <- matrix(c(1, 0.71, 0.71, 2), nrow = 2)

## Generate piecewise stationary time series
ts.Phi <- function(Phi) rARMA(2^9, 2, Phi, Theta, Sigma)$X
ts <- rbind(ts.Phi(Phi1), ts.Phi(Phi2))

pgram <- pdPgram2D(ts)