This paper studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time dependent conditional variances. This TVP model is flexible to accommodate a wide variety of timevariation patterns but requires effective shrinkage...

The mixture innovation (MI) model places spike-and-slab mixture distributions on the innovations of time-varying regression coefficients and allows for a wide variety of time variation patterns. Despite its appeal, estimating the MI model can be computationally prohibitive when the number of...