We introduce a high-dimensional structural time series model, where co-movement between the components is due to common factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step and state space methods in a second step. The...

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models...

This paper describes a dynamic factor model for the Maltese economy. The model mainly serves as a tool to timely provide the Central Bank of Malta with nowcasts as well as short-term forecasts of the growth rate of the real gross domestic product, which in turn are used as an input in the...