This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...

Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP...

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...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The positive relationship between real exchange rates and natural resource income is well understood and studied. However, climate change and the transition to a lower-carbon economy now challenges this relationship. We document this by proposing a novel news media-based measure of climate...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...