We incorporate external information extracted from the European Central Bank's Survey of Professional Forecasters into the predictions of a Bayesian VAR, using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density...

This paper studies the informational content of speeches of Fed officials, focusing on financial stability, from 1997 to 2018. We construct indicators that measure the intensity and tone of this topic for both Governors and Fed presidents. When added to a standard forward-looking Taylor rule, a...

We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and...

Surveys of Professional Forecasters produce precise and timely point forecasts for key macroeconomic variables. However, the accompanying density forecasts are not as widely utilized, and there is no consensus about their quality. This is partly because such surveys are often conducted for...

How should researchers combine predictive densities to improve their forecasts? I propose consistent estimators of weights which deliver density forecast combinations approximating the true predictive density, conditional on the researcher's information set. Monte Carlo simulations confirm that...

In this paper we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models. Importantly our framework covers the local projections — instrumental variable model...

We present a house price-at-risk (HaR) model that fits the historical developments in the Spanish housing market. By means of quantile regressions we show that a model including quarterly real house price growth, a misalignment measure and a consumer confidence index is able to accurately...