GIBBSPROBITDYNAMIC.RPF estimates using Bayesian methods a probit model with the latent index variable assumed to follow an AR(1) process. This uses the typical Gibbs sampling technique of drawing the continuous latent variable given the observable 0-1's, but, because of the dynamic process for the latent variable, you can't just draw those independently. Instead, the YSTAR are done one-at-a-time given the values of the YSTAR at all other data points.
Detailed Description
GIBBSPROBITDYNAMIC—Gibbs Sampling for Dynamic Probit
GIBBSPROBITDYNAMIC—Gibbs Sampling for Dynamic Probit
Last bumped by TomDoan on Sun Sep 08, 2024 10:04 am.