CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet)...
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Research Clusters
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On the Empirics of Reserve Requirements and Economic Growth
Jesús Crespo Cuaresma, Gregor von Schweinitz, Katharina Wendt
Journal of Macroeconomics,
June
2019
Abstract
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 long-run growth rates of GDP per capita and credit (%GDP) making use of Bayesian model averaging methods. Regulation has on average a negative effect on GDP in tranquil times, which is only partly offset by a positive (but not robust effect) in crisis times. Credit over GDP is positively affected by higher requirements in the longer run.
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01.04.2019 • 8/2019
Bank profitability increases after eliminating consolidation barriers
When two banks merge because political consolidation barriers are abolished, the combined entity is considerably more profitable and useful to the real economy. This is the headline result of an analysis of compulsory savings banks mergers carried out by the Halle Institute for Economic Research (IWH). The study yields important insights for the German and the European banking market.
Michael Koetter
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IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct...
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
IWH Discussion Papers,
No. 2,
2019
Abstract
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.
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Reports of the European Forecasting Network (EFN)
Reports of the European Forecasting Network (EFN) The European Forecasting...
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Ludwig (Interview)
About the CIA and a glass of red wine ... Professor Dr Udo Ludwig on the...
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