Why Are Households Saving so much During the Corona Recession?
IWH Policy Notes,
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.
Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
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.
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.
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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).
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Does Machine Learning Help us Predict Banking Crises?
Journal of Financial Stability,
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.
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.
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Information Feedback in Temporal Networks as a Predictor of Market Crashes
In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.