SimHMMGaussianInv: Simulation of a univariate Gaussian Hidden Markov Model (HMM)

In GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

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 Rémillard, 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)

GaussianHMM1d documentation built on July 9, 2023, 6:52 p.m.