create_prior_parameters: Create the prior parameters.

In bltm: Bayesian Latent Threshold Modeling

Description

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

Usage

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.

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)

Value

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

References

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

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