.BayesFitTFP: Estimates the parameters and states of a two-dimensional state-space model by Bayesian methods to obtain the tfp trend.

Description

Estimates the parameters and states of a two-dimensional state-space model by Bayesian methods to obtain the tfp trend.

Usage

.BayesFitTFP(
  model,
  prior = initializePrior(model),
  R = 10000,
  burnin = ceiling(R/10),
  thin = 1,
  HPDIprob = 0.85,
  FUN = mean,
  MLEfit = NULL
)

Arguments

model: An object of class TFPmodel.
prior: A list of matrices with parameters for the prior distribution and box constraints. By default, prior is initialized by initializePrior(model). See details. Only used if method = "bayesian".
R: An integer specifying the number of MCMC draws. The default is R = 10000. Only used if method = "bayesian".
burnin: An integer specifying the burn-in phase of the MCMC chain. The default is burnin = ceiling(R / 10). Only used if method = "bayesian".
thin: An integer specifying the thinning interval between consecutive draws. The default is thin = 1, implying that no draws are dopped. For thin = 2, every second draw is dropped and so on. Only used if method = "bayesian".
HPDIprob: A numeric in the interval (0,1) specifying the target probability of the highest posterior density intervals. The default is HPDIprob = 0.9. Only used if method = "bayesian".
FUN: A function to be used to compute estimates from the posterior distribution. Possible options are "mean" and "median". The default is FUN = "mean". Only used if method = "bayesian".
MLEfit: (Optional) An object of class TFPfit which is used for initialization. Only used if method = "bayesian".