This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including...

We use a flexible Bayesian model averaging method to estimate a factor pricing model characterized by structural uncertainty and instability in macro-financial factor loadings and idiosyncratic risks. We propose such a framework to investigate key differences in the pricing mechanism that...

The accuracy of real-time forecasts of macroeconomic variables that are subject to revisions may crucially depend on the choice of data used to compare the forecasts against. We put forward a flexible time - varying parameter regression framework to obtain early estimates of the final value of...

The accuracy of real-time forecasts of macroeconomic variables that are subject to revisions may crucially depend on the choice of data used to compare the forecasts against. We put forward a flexible time-varying parameter regression framework to obtain early estimates of the final value of...

A Bayesian nonparametric predictive model is introduced to construct time-varying weighted combinations of a large set of predictive densities. A clustering mechanism allocates these densities into a smaller number of mutually exclusive subsets. Using properties of Aitchinson's geometry of the...

Interconnections between Eurozone and United States booms and busts and among major Eurozone economies are analyzed using a Panel Markov-Switching VAR model. The model accommodates changes in low and high data frequencies and incorporates endogenous time-varying transition matrices of...

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...