gratis (version 1.0.7)

rmixnorm_ts: Simulate autoregressive random variables from mixture of normal

Description

This function simulates random samples from a finite mixture of Gaussian distribution where the mean from each components are AR(p) process.

Usage

rmixnorm_ts(n, means.ar.par.list, sigmas.list, weights, yinit = 0)

Value

vector of length n follows a mixture distribution.

Arguments

n

number of samples.

means.ar.par.list

parameters in AR(p) within each mixing compoment.

sigmas.list

variance list.

weights

weight in each list.

yinit

initial values.

Author

Feng Li, Central University of Finance and Economics.

References

Feng Li, Mattias Villani, and Robert Kohn. (2010). Flexible Modeling of Conditional Distributions using Smooth Mixtures of Asymmetric Student T Densities, Journal of Statistical Planning and Inference, 140(12), pp. 3638-3654.

Examples

if (FALSE) { # require("fGarch", quietly = TRUE)
n <- 1000
means.ar.par.list <- list(c(0, 0.8), c(0, 0.6, 0.3))
require("fGarch")
sigmas.spec <- list(
  fGarch::garchSpec(model = list(alpha = c(0.05, 0.06)), cond.dist = "norm"),
  fGarch::garchSpec(model = list(alpha = c(0.05, 0.05)), cond.dist = "norm")
)
sigmas.list <- lapply(
  lapply(sigmas.spec, fGarch::garchSim, extended = TRUE, n = n),
  function(x) x$sigma
)
weights <- c(0.8, 0.2)
y <- rmixnorm_ts(
  n = n, means.ar.par.list = means.ar.par.list, sigmas.list = sigmas.list,
  weights = weights
)
plot(y)
}