Vector Autoregression with Mixed Frequency Data

Three new approaches are proposed to handle mixed frequency Vector Autoregression. The first is an explicit solution to the likelihood and posterior distribution. The second is a parsimonious, time-invariant and invertible state space form. The third is a parallel Gibbs sampler without forward filtering and backward sampling. The three methods are unified since all of them explore the fact that the mixed frequency observations impose linear constraints on the distribution of high frequency latent variables. By a simulation study, different approaches are compared and the parallel Gibbs sampler outperforms others. A financial application on the yield curve forecast is conducted using mixed frequency macro-finance data.