rmixnorm_ts: Simulate autoregressive random variables from mixture of...

In gratis: Generating Time Series with Diverse and Controllable Characteristics

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)

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.

Value

vector of length n follows a mixture distribution.

Author(s)

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

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)

gratis documentation built on May 29, 2024, 5:36 a.m.