SimHMMGaussianInv {GaussianHMM1d} | R Documentation |
Simulation of a univariate Gaussian Hidden Markov Model (HMM)
Description
Generates a univariate regime-switching random walk with Gaussian regimes starting from a given state eta0, using the inverse method from noise u.Can be useful when generating multiple time series.
Usage
SimHMMGaussianInv(u, mu, sigma, Q, eta0)
Arguments
u |
series of uniform i.i.d. series (n x 1); |
mu |
vector of means for each regime (r x 1); |
sigma |
vector of standard deviations for each regime (r x 1); |
Q |
Transition probality matrix (r x r); |
eta0 |
Initial value for the regime; |
Value
x |
Simulated Data |
eta |
Probability of regimes |
Author(s)
Bouchra R Nasri and Bruno N RĂ©millard, January 31, 2019
References
Nasri & Remillard (2019). Copula-based dynamic models for multivariate time series. JMVA, vol. 172, 107–121.
Examples
Q <- matrix(c(0.8, 0.3, 0.2, 0.7),2,2)
set.seed(1)
u <-runif(250)
mu <- c(-0.3 ,0.7)
sigma <- c(0.15,0.05);
eta0=1
x <- SimHMMGaussianInv(u,mu,sigma,Q,eta0)
[Package GaussianHMM1d version 1.1.1 Index]