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.
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Why Are Households Saving so much During the Corona Recession?
Reint E. Gropp, William McShane
IWH Policy Notes,
No. 1,
2021
Abstract
Savings rates among European households have reached record levels during the Corona recession. We investigate three possible explanations for the increase in household savings: precautionary motivations induced by increased economic uncertainty, reduced consumption opportunities due to lockdown measures, and Ricardian Equivalence, i.e. increases in the expected future tax-burden of households driven by increases in government debt. To test these explanations, we compile a monthly panel of euro area countries from January 2019 to August 2020. Our findings indicate that the chief driver of the increase in household savings is supply: As governments restrict households’ opportunities to spend, households spend less. We estimate that going from no lockdown measures to that of Italy’s in March, would have resulted in the growth of Germany’s deposit to Gross Domestic Product (GDP) ratio being 0.6 percentage points higher each month. This would be equivalent to the volume of deposits increasing by roughly 14.3 billion euros or 348 euros per house monthly. Demand effects, driven by either fears of unemployment or fear of infection from COVID-19, appear to only have a weak impact on household savings, whereas changes in government debt are unrelated or even negatively related to savings rates. The analysis suggests that there is some pent-up demand for consumption that may unravel after lockdown measures are abolished and may result in a significant increase in consumption in the late spring/early summer 2021.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
No. 1,
2021
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (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 simulated and real-world evidence that this simplification results in stable thresholds and improves 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.
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06.08.2020 • 15/2020
IWH Bankruptcy Update: Number of Employees Affected by Bankruptcy Continues to Rise in Germany
In July, more than three times as many jobs were impacted by corporate bankruptcies in Germany in comparison to the monthly averages from early 2020. The July figure was also significantly higher in relation to the previous month. By contrast, the number of bankruptcies fell slightly. These are the main findings of the most recent IWH Bankruptcy Update published by the Halle Institute for Economic Research (IWH), which provides monthly reports on German bankruptcies.
Steffen Müller
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Corona Shutdown and Bankruptcy Risk
Oliver Holtemöller, Yaz Gulnur Muradoglu
IWH Online,
No. 3,
2020
Abstract
This paper investigates the consequences of shutdowns during the Corona crisis on the risk of bankruptcy for firms in Germany and United Kingdom. We use financial statements from the period 2014 to 2018 to predict how pervasive risk of bankruptcy becomes for micro, small, medium, and large firms due to shutdown measures. We estimate distress for firms using their capacity to service their debt. Our results indicate that under three months of shutdown almost all firms in shutdown industries face high risk of bankruptcy. In Germany, about 99% of firms in shutdown industries and in the UK about 98% of firms in shutdown industries are predicted to be under distress. The furlough schemes reduce the risk of bankruptcy only marginally to 97% of firms in shutdown industries in Germany and 95% of firms in shutdown industries in the United Kingdom in case of a three-month shutdown. In sectors that are not shutdown under conservative estimates of contagion of sales losses, our results indicate considerable risk of widespread bankruptcies ranging from 76% of firms in Germany to 69% of firms in the United Kingdom. These early findings suggest that the impact of corona crisis on corporate sector via shutdowns can be severe and subsequent policy should be designed accordingly.
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05.06.2020 • 8/2020
IWH Bankruptcy Update: Increase in large firm bankruptcies
With overall corporate bankruptcies remaining constant, ever more employees are subject to employer bankruptcy in Germany. This is the latest insight from the IWH Bankruptcy Update provided monthly by the Halle Institute for Economic Research (IWH).
Steffen Müller
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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.
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12.12.2019 • 24/2019
Global economy slowly gains momentum – but Germany still stuck in a downturn
In 2020, the global economy is likely to benefit from the recent thaw in trade disputes. Germany’s manufacturing sector, however, will recover only slowly. “In 2020, the German economy will probably grow at a rate of 1.1%, and adjusted for the unusually high number of working days the growth rate will only be 0.7%”, says Oliver Holtemöller, head of the Department Macroeconomics and vice president at Halle Institute for Economic Research (IWH). With an estimated growth rate of 1.3%, production in East Germany will outpace total German production growth.
Oliver Holtemöller
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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
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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