View source: R/dynamicWhittle_prior_and_mcmc_params.R
psd_tvarma12 | R Documentation |
time-varying spectral density function of the tvARMA(1,2) processes for illustrations
psd_tvarma12(
rescaled_time,
freq,
dgp = NULL,
a1 = function(u) {
rep(0, length(u))
},
b1 = function(u) {
rep(0, length(u))
},
b2 = function(u) {
rep(0, length(u))
}
)
rescaled_time , freq |
numeric vectors forming a rectangular grid on which the tv-PSD is evaluated. |
dgp |
optional: the tv-ARMA models demonstrated in section 4.2 of Tang et al. (2023). Should be chosen from "LS1", "LS2" and "LS3". See section Details. |
a1 , b1 , b2 |
If dgp is not supplied, these arguments can be used to specify customized tv-ARMA
process (up to order(1,2)). See Details.
rescaled_time must be in |
See sim_tvarma12 for the precise definition of a tvARMA(1,2) process. The time-varying spectral density function of this process is defined as
where u
is called rescaled time and \lambda
is called frequency.
For dgp = "LS1", it is a tvMA(2) process (MA order is 2) with
For dgp = "LS2", it is a tvMA(1) process (MA order is 1) with
For dgp = "LS3", it is a tvAR(1) process (MA order is 0) with
a matrix of dimension length(rescaled_time) by length(freq).
Tang et al. (2023) Bayesian nonparametric spectral analysis of locally stationary processes ArXiv preprint <arXiv:2303.11561>
## Not run:
res_time <- seq(0, 1, by = 0.005); freq <- pi*seq(0, 1, by = 0.01)
true_tvPSD <- psd_tvarma12(rescaled_time = res_time, freq = freq, dgp = "LS2")
plot(true_tvPSD)
## End(Not run)
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