This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced...

This paper explores the relationship between policy settings and extreme positive and negative growth events, what we call GDP tail risks, using quantile regression methods. Conditioning on several country characteristics such as the size, stage of development and openness to trade as well as...

We design a statistical model for measuring the homogeneity of a financial network that evolves over time. Our model focuses on the level of diversi- fication of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather...

This paper investigates in a non-linear setting the impact on the real economy of frictions stemming from the financial sector. We develop a medium scale DSGE model with a banking sector where an occasionally binding constraint on banks' capital induces a relevant non-linearity. The model -...

In this paper we estimate a Bayesian vector autoregressive model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area. This allows us to treat macroeconomic uncertainty as a latent quantity during estimation. Only a limited number of...

I examine the relevance of contagion in explaining financial distress in the US banking system by identifying the component of bank level probabilities that is due to contagion. Identification is achieved after controlling for macrofinancial and bank specific shocks that have similar...