A Dynamic Binary Probit Model with Time-Varying Parameters and Shrinkage Prior
采用收缩先验的时变参数动态二元 Probit 模型
This article studies a time series binary probit model in which the underlying latent variable depends on its lag and exogenous regressors. The regression coefficients for the latent variable are allowed to vary over time to capture possible model instability. Bayesian shrinkage priors are applied to automatically differentiate fixed and truly time-varying coefficients and thus avoid unnecessary model complexity. I develop an MCMC algorithm for model estimation that exploits…