We address the problem of dynamic variable selection in time series regression with unknown residual variances, where set active predictors is allowed to evolve over time. To capture time-varying uncertainty, we introduce new shrinkage priors for coefficients. These are characterized by two main ingredients: smooth parameter evolutions and intermittent zeroes modeling predictive breaks. More fo...