In this paper we estimate a simple Bayesian learning model to expectations data from the Survey of Professional Forecasters. We reformulate the model in terms of forecast revisions, which allows to abstract from differences in priors and to focus the analysis on the relationship between...

I evaluate whether expectations of professional forecasters are consistent with the property of Bayesian learning that the expected uncertainty of a fixed target forecast should decline with the horizon. I obtain a measure of individual uncertainty from the density forecasts of the Survey of...

In this paper we investigate the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semi-parametric specification based on the Quantile Auto-Regressive (QAR) model that assumes that the...

We evaluate the informational content of news-based sentiment indicators for forecasting the Gross Domestic Product (GDP) of the five major European economies. The sentiment indicators that we construct are aspect-based, in the sense that we consider only the text that is related to a specific...