Modelling and forecasting economic time series with mixed causal-noncausal models

This thesis investigates the forecasting ability of mixed causal-noncausal (MAR) models. This type of models can be employed to model and forecast financial and economic time series that are for instance characterized by bubbles. Bubbles (a persistent increase followed by a sudden crash) can have dramatic impacts, an example is the U.S. housing market bubble that led to a global financial crisis. The models employed in this thesis are simple to use and offer a large flexibility. Since MAR models are still rather new, the literature on some aspects remains scarce. This thesis thus first focuses on predictions, with applications ranging from extreme episodes to more stable ones. MAR models can be employed in various areas of applications and the predictive densities obtained from them can for instance be used to construct risk measures or credibility indices of Central bank. The thesis also investigates extensions of the model, such as the use of external variables or employing the model in a multivariate setting.