create_prior_parameters function

Create the prior parameters.

Define the priors parameters to be used with ltm_mcmc().

create_prior_parameters(a_mu0 = 0, a_s0 = 0.1, n0 = 6, S0 = 0.06,
  v0 = 6, V0 = 0.06, m0 = 0, s0 = 1, a0 = 20, b0 = 1.5)

Arguments

• a_mu0: mean of alpha normal distribution.
• a_s0: standard deviation of alpha's normal distribution.
• n0: sig2 inverse gamma shape parameter.
• S0: sig2 inverse gamma location parameter.
• v0: sig_eta inverse gamma shape parameter.
• V0: sig_eta inverse gamma location parameter.
• m0: mu normal's mean parameter.
• s0: mu normals standard deviation.
• a0: a0 beta's shape parameter.
• b0: a0 beta's location parameter.

Returns

List containing the hyperparameters used to fit the model. The default parameters are the same of the simulation example of the paper.

Details

Considering the following priors:

• alpha ~ N(mu0, s0)
• sig2 ~ IG(n0/2, S0/2)
• sig_eta ~ IG(v0/2, V0/2)
• mu ~ N(m0, s0^2)
• (phi+1)/2 ~ Beta(a0, b0)

References

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