A Dynamic Binary Probit Model with Time-Varying Parameters and Shrinkage Prior
研究了一个时变系数的二元Probit模型，通过贝叶斯收缩先验自动区分固定和时变系数，并开发了高效的MCMC算法和基于卡尔曼滤波的预测似然计算方法，用于预测经济衰退。
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 parameter blocking to boost sampling efficiency. An efficient Monte Carlo approximation based on the Kalman filter is developed to improve the numerical stability for computing the predictive likelihood of the binary outcome. Benefits of the proposed model are illustrated in a simulation study and an application to forecast economic recessions.