ltm_mcmc function

MCMC LTM

Given x and y performs the MCMC optimization.

ltm_mcmc(x, y, burnin = 2000, iter = 8000, K = 3,
  prior_par = create_prior_parameters())

Arguments

• x: data points
• y: response variable
• burnin: number of burnin iterations
• iter: number of iterations after burnin
• K: parameter K
• prior_par: List of parameters for prior distrributions. See create_prior_parameters().

Returns

matrix containing the posterior samples. Each line is one sample after the burnin period and each column is one of the parameters of the model. Columns are named to find the parameters with ease.

Examples

# Generates 10 series, each one with 500 observations and 2 regressors.

d_sim <- ltm_sim(
  ns = 500, nk = 2, ni = 10,
  vmu = matrix(c(.5,.5), nrow = 2),
  mPhi = diag(2) * c(.99, .99),
  mSigs = c(.1,.1),
  dsig = .15,
  vd = matrix(c(.4,.4), nrow = 2),
  alpha = 0
)

# Fit model

fit_model <- ltm_mcmc(d_sim$mx, d_sim$vy, burnin = 0, iter = 2)

References

Nakajima, Jouchi, and Mike West. "Bayesian analysis of latent threshold dynamic models." Journal of Business & Economic Statistics 31.2 (2013): 151-164.