We employ a nonlinear VAR framework and a state-of-the-art identification strategy to document the large response of real activity to a financial uncertainty shock during and in the aftermath of the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept...

We estimate a three-variate VAR using proxies of global financial uncertainty, the global financial cycle, and world industrial production to simulate the effects of the jump in financial uncertainty observed in correspondence of the Covid-19 outbreak. We predict the cumulative loss in world...

We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set-identification via a combination of...

How do short and long term interest rates respond to a jump in financial uncertainty? We address this question by conducting a local projections analysis with US monthly data, period: 1962-2018. The state-of-the-art financial uncertainty measure proposed by Ludvigson, Ma, and Ng (2019) is found...

We examine the relative importance of accuracy concerns and belief confirmation motives in driving the demand for news. In experiments with US respondents, we first vary beliefs about whether an outlet reports the news in a right-wing biased, left-wing biased, or unbiased way. We then measure...

We study the effects of monetary policy on aggregate consumption combining a heterogeneous agent model with measured expectations under different policy counterfactuals. We express the consumption of non-hand-to-mouth households as a function of expectations only and elicit all expectations...

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results...

We conduct an experiment with a representative sample from the US to study households’ demand for macroeconomic information. Respondents who learn of a higher personal exposure to unemployment risk during recessions increase their demand for an expert forecast about the likelihood of a...

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...