In this paper, we compare the performance of two non-parametric methods of classification, Regression Trees (CART) and the newly Multivariate Adaptive Regression Splines (MARS) models, in forecasting bankruptcy. Models are implemented on a large universe of US banks over a complete market cycle...

The paper examines episodes of current account adjustment in individual economies. A central finding is that these episodes are very divergent and can be usefully classified, on the basis of cluster analysis, in three groups. A majority of cases is characterised by internal adjustment,...

The paper examines over seventy episodes of current account adjustment in industrial and major emerging market economies. It argues that these episodes were characterised by strongly divergent economic developments. To reduce this divergence, the paper classifies episodes with similar...

We develop a flexible nonparametric similarity-based approach to predict the state of the business cycle in different interest rate environments. Our approach provides methodological advantages over parametric logit and probit models and new empirical perspectives on the usefulness of the term...

Behavioral changes due to digitalization, such as telework, shifted energy demand, especially during COVID-19. Behavioral changes are often overlooked in macro-demand forecasts. This study forecasts macro-energy use to 2030 using American Time Use Survey and national energy data from 2003-2019....

Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some...