We use the World Input Output Database (WIOD) to estimate the potential employment effects of a hard Brexit in 43 countries. In line with other studies we assume that imports from the European Union (EU) to the UK will decline by 25% after a hard Brexit. The absolute effects are largest in big EU countries which have close trade relationships with the UK like Germany and France. However, there are also large countries outside the EU which are heavily affected via global value chains like China, for example. The relative effects (in percent of total employment) are largest in Malta and Ireland. UK employment will also be affected via intermediate input production. Within Germany, the motor vehicle industry and in particular the “Autostadt” Wolfsburg are most affected.

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance.

The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Based on a synthetic matching framework, we analyse the impact of this unexpected (and therefore exogenous) shock on the stock market. The results reveal a significant level shift (decline) in asset prices in Switzerland following the discontinuation of the minimum exchange rate. While adjustments in stock market returns were most pronounced directly after the news announcement, the variance was elevated for some weeks, indicating signs of increased uncertainty and potentially negative consequences for the real economy.

Reserve requirements, as a tool of macroprudential policy, have been increasingly employed since the outbreak of the great financial crisis. We conduct an analysis of the effect of reserve requirements in tranquil and crisis times on credit and GDP growth making use of Bayesian model averaging methods. In terms of credit growth, we can show that initial negative effects of higher reserve requirements (which are often reported in the literature) tend to be short-lived and turn positive in the longer run. In terms of GDP per capita growth, we find on average a negative but not robust effect of regulation in tranquil times, which is only partly offset by a positive but also not robust effect in crisis times.