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...

This paper extends the methodology for a simultaneous detecting of business and growth cycle, already developed for the euro area, to its major member countries. The best performing indicators for each country are identified through a simulation exercise. An indirect pair of turning point...

In the paper we introduce a system for statistical detection of turning points in the euro area based on the class of multivariate Markov-switching models. Component series considered in the application are monthly short term statistics released by Eurostat, business and consumer surveys...

This paper proposes a panel Markov-Switching (MS-) VAR model suitable for a multi-country analysis of the business cycle. We study the business cycles fluctuations of a group of countries, analyse the transmission of shocks across cycles and predict the turning points of the country-specific...