East Germany
The Nasty Gap 30 years after unification: Why East Germany is still 20% poorer than the West Dossier In a nutshell The East German economic convergence process is hardly…
See page
Privacy
Data Protection Policy We take the protection of your personal data very seriously and treat your personal data with confidentiality and in compliance with the provisions of law…
See page
Ludwig (Interview)
About the CIA and a glass of red wine ... Professor Dr Udo Ludwig on the beginnings of our institute The core of the IWH founding team came from the Institute for Applied Economic…
See page
Speed Projects
Speed Projects On this page, you will find the IWH EXplore Speed Projects in chronologically descending order. 2021 2020 2019 2018 2017 2016 2015 2014 2021 SPEED 2021/01…
See page
Projects
Our Projects 07.2022 ‐ 12.2026 Evaluation of the InvKG and the federal STARK programme On behalf of the Federal Ministry of Economics and Climate Protection, the IWH and the RWI…
See page
The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
Geraldine Dany-Knedlik, Martina Kämpfe, Tobias Knedlik
Empirica,
No. 1,
2021
Abstract
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.
Read article
Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
No. 1,
2021
Abstract
Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
Read article
Private Equity and Portfolio Companies: Lessons From the Global Financial Crisis
Shai B. Bernstein, Josh Lerner, Filippo Mezzanotti
Journal of Applied Corporate Finance,
No. 3,
2020
Abstract
Critics of private equity have warned that the high leverage often used in PE-backed companies could contribute to the fragility of the financial system during economic crises. The proliferation of poorly structured transactions during booms could increase the vulnerability of the economy to downturns. The alternative hypothesis is that PE, with its operating capabilities, expertise in financial restructuring, and massive capital raised but not invested ("dry powder"), could increase the resilience of PE-backed companies. In their study of PE-backed buyouts in the U.K. - which requires and thereby makes accessible more information about private companies than, say, in the U.S. - the authors report finding that, during the 2008 global financial crisis, PE-backed companies decreased their overall investments significantly less than comparable, non-PE firms. Moreover, such PE-backed firms also experienced greater equity and debt inflows, higher asset growth, and increased market share. These effects were especially notable among smaller, riskier PE-backed firms with less access to capital, and also for those firms backed by PE firms with more dry powder at the crisis onset. In a survey of the partners and staff of some 750 PE firms, the authors also present compelling evidence that PEs firms play active financial and operating roles in preserving or restoring the profitability and value of their portfolio companies.
Read article
Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
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 metric, 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 efficiently, 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.
Read article
An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
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.
Read article
