Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB's SPF and Consensus Economics compared to their...

We study how millions of highly granular and weekly household scanner data combined with novel machine learning techniques can help to improve the nowcast of monthly German inflation in real time. Our nowcasting exercise targets three hierarchy levels of the official consumer price index. First,...

Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB's SPF and Consensus Economics compared to their...

The general view underlying bank regulation is that bank disclosures providemarket discipline and reduce banks' risk-taking incentives. We show that bankdisclosures can increase bank leverage and bank risk. The reason stems from theinteraction between insured and uninsured debt. Bank disclosures...

The general view underlying bank regulation is that bank disclosures providemarket discipline and reduce banks' risk-taking incentives. We show that bankdisclosures can increase bank leverage and bank risk. The reason stems from theinteraction between insured and uninsured debt. Bank disclosures...

The linear pool is the most popular method for combining density forecasts. We analyze the linear pool's implications concerning forecast uncertainty in a new theoretical framework that focuses on the mean and variance of each density forecast to be combined. Our results show that, if the...

We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and...

We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between...

We follow the idea of exploiting cross-sectional information to improve recession probability forecasts by aggregating indicator-specific turning point predictions to obtain economy-wide recession probabilities. This stands in contrast to most of the relevant literature, which relies on an...

The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting...