Lately, several approaches to state estimation and parameter change detection based on the utilization of a finite number of process models appeared. The paper compares these methods in a unified Bayesian framework.

This thesis focusses on econometric applications requiring multivariate numerical integration. Models that attempt to capture real world complexities are typically nonlinear and display many unobservable factors. These characteristics imply that the likelihood function of these models contain...

Stochastic volatility (SV) models provide a means of tracking and forecasting the variance of financial asset returns. While SV models have a number of theoretical advantages over competing variance modelling procedures they are notoriously difficult to estimate. The distinguishing feature of...

We propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weights are...

This paper compares two classes of models that allow for additional channels of correlation between asset returns: regime switching models with jumps and models with contagious jumps. Both classes of models involve a hidden Markov chain that captures good and bad economic states. The distinctive...