In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

We model capital flows among Chinese provinces using a theory-based variance decomposition that allows us to gauge the importance of various channels of external adjustments at the regional level: variation in intertemporal prices-domestic and international interest rates and the real exchange...

We model capital flows among Chinese provinces using a theory-based variance decomposition that allows us to gauge the importance of various channels of external adjustments at the regional level: variation in intertemporal prices—domestic and international interest rates and the real exchange...

We consider a prototypical representative-agent forward-looking model, and study the low frequency variability of the data when the agent's beliefs about the model are updated through linear learning algorithms. We find that learning in this context can generate strong persistence. The degree of...