Comments and suggestions from the conference organizers, the editor, and referees have helped improve the paper Generous support from the Institute for New Economic Thinking, the Bundesministerium für Bildung und Forschung (BMBF), and the Volkswagen Foundation supported our work. We are grateful for their support. The views expressed in this paper are the sole responsibility of the authors and to not necessarily reflect the views of the Federal Reserve Bank of San Francisco, the Federal Reserve System, or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractTo improve the detection of the economic "danger zones" from which severe banking crises emanate, this paper introduces classification tree ensembles to the banking crisis forecasting literature. I show that their out-of-sample performance in forecasting binary banking crisis indicators surpasses current best-practice early warning systems based on logit models by a substantial margin. I obtain this result on the basis of one long-run-(1870-2011), as well as two broad post-1970 macroeconomic panel datasets. I particularly show that two marked improvements in forecasting performance result from the combination of many classification trees into an ensemble, and the use of many predictors.

Can central banks defuse rising stability risks in financial booms by leaning against the wind with higher interest rates? This paper studies the state-dependent effects of monetary policy on financial crisis risk. Based on the near-universe of advanced economy financial cycles since the nineteenth century, we show that discretionary leaning against the wind policies during credit and asset price booms are more likely to trigger crises than prevent them. (JEL E43, E44, E52, E58, F33)

We use panel vector autoregressions to analyze the underlying structure of changes in subjective well‐being and its coevolution with changes in income, health, worries, marital status and employment status for the German Socio‐Economic Panel (SOEP) data set. We find that positive changes in the named life domains are followed by decreases in subjective well‐being (except for health, which is followed by well‐being increases). Positive changes in well‐being are followed by positive changes in most life domains. We also examine how the structure of subjective well‐being differs with respect to different Big Five personality traits.