This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood...

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’. The...

This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of New Keynesian Dynamic Stochastic General Equilibrium (NK DSGE) models with multiple regimes. We outline the theoretical...