simuHMM function

Simulate Hidden Markov model with normally distributed errors

This function simulates a Hidden Markov Model process.

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.
  • Z: A (T x qz) matrix with exogenous regressors (Optional) and where qz is the number of exogenous variables.
  • betaZ: A (qz x q) matrix true coefficients on exogenous regressors (Optional) and where qz is the number of exogenous variables.

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

Returns

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)