Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach – We apply six...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...

This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine...

This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank on-site credit examination to compare the performance of a hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external...

Uncertainty may affect economic behavior of individuals and firms in a wide variety of ways, with typically negative consequences for economic growth. It is due to this fact, combined with rising political uncertainty observed lately in many countries, that uncertainty has gained increasing...

We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types of machine learning algorithms as well as traditional choice modeling approaches. The SCPMs are recorded during a fixed...

Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their...

I propose a novel approach to uncover business cycle reports' priorities and relate them to economic fluctuations. To this end, I leverage quantitative business-cycle forecasts published by leading German economic research institutes since 1970 to estimate the proportions of latent topics in...