This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to...

We adopt a time series approach to investigate the historical relation between unemployment, life expectancy, and mortality rates. We fit Vector-autoregressions for the overall US population and for groups identified based on gender and race. We use our results to assess the long-run effects of...

We embed a news shock, a noisy indicator of the future state, in a two-state Markovswitching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a...