The paper investigates the importance of modeling in cay estimations from a statistical and economic perspective by observing the stochastic trend, a thus far neglected component. In order to do this, we perform an empirical analysis on US secular annual data from 1900 to 2015 considering the...

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying...

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven...