simuHMM {MSTest}

Simulate Hidden Markov model with normally distributed errors

Description

This function simulates a Hidden Markov Model process.

Usage

simuHMM(mdl_h0, burnin = 100)

Arguments

mdl_h0

List containing the following DGP parameters

• n: Length of series.

• k: Number of regimes.

• mu: A (k x q) vector of means.

• sigma: A (q x q) covariance matrix.

• q: Number of series.

• P: A (k x k) transition matrix (columns must sum to one).

• eps: An optional (T+burnin x q) matrix with standard normal errors to be used. Errors will be generated if not provided.

burnin

Number of simulated observations to remove from beginning. Default is 100.

Value

List with simulated series and its DGP parameters.

Examples

set.seed(1234)

# ----- Univariate ----- #
# Define DGP 
mdl_hmm <- list(n     = 1000, 
                q     = 1,
                mu    = as.matrix(c(5,
                                   -2)),
                sigma = list(as.matrix(5.0),
                             as.matrix(7.0)),
                k     = 2,
                P     = rbind(c(0.90, 0.10),
                              c(0.10, 0.90)))

# Simulate process using simuHMM() function
y_hmm_simu <- simuHMM(mdl_hmm)

plot(y_hmm_simu)

# ----- Multivariate ----- #
# Define DGP 
mdl_hmm <- list(n     = 1000, 
                q     = 2,
                mu    = rbind(c(5, -2),
                              c(10, 2)),
                sigma = list(rbind(c(5.0, 1.5),
                                   c(1.5, 1.0)),
                             rbind(c(7.0, 3.0),
                                   c(3.0, 2.0))),
                k     = 2,
                P     = rbind(c(0.90, 0.10),
                              c(0.10, 0.90)))

# Simulate process using simuHMM() function
y_hmm_simu <- simuHMM(mdl_hmm)


plot(y_hmm_simu)