ltm_mcmc: MCMC LTM

In bltm: Bayesian Latent Threshold Modeling

Description

Given x and y performs the MCMC optimization.

Usage

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().

Value

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.

References

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

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)

bltm documentation built on July 18, 2019, 9:06 a.m.