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We examine the effects of the COVID-19 pandemic on the economic decline expected in Germany in 2020. The magnitude of the economic slump that will occur in 2020 depends on the extent of the slump during the shutdown, on the point in time, at which a significant easing of shutdown occurs, and on the length of adjustment process towards the structures that prevailed before the pandemic. We derive several scenarios and find that the shutdown will only remain in the single-digit percentage range if we apply very optimistic assumptions about the extent of the initial decline in GDP during the shutdown and the speed of adjustment after opening up of the economy. However, assuming that the economic crisis cannot end before the medical crisis ends, which medical experts project not to happen before the end of 2020, such optimistic assumptions do not appear realistic. Hence, we find it more likely that the percentage decline of GDP in Germany will be two-digit in 2020. Our findings are in contrast to the growth projections recently issued by the German Council of Economic Experts or by the Federal Ministry of Economic Affairs and Energy of Germany.
This paper examines the recession probabilities for the Eurozone along four different dimensions: First, we identify the best performing indicators for a recession within the next 12 months based on 43 underlying single variables and their different transformations in a benchmark model. We find that a modified version of the yield curve incorporating the shadow interest rate removes the downward rigidity of the front-leg and restores part of the informational content of the term spread at the zero lower bound. However, the best performing single indicator of the benchmark model is Real M1 followed by the Purchasing Managers Index (PMI), the investment grade corporate bond spread and the Terms of Trade. Second, the paper establishes three submodels to increase the lead-time and the stability of recession models: (i) Monetary transmission channels via principal component analysis; (ii) Bivariate regressions to identify paramount combinations; (iii) Unstable surges vis-à-vis the Hodrick-Prescott trend to detect animal spirits and hawkish mistakes. Third, the analysis is extended over various forecasting horizons (6m, 18m and 24m). Fourth, the results are analyzed from the perspective of risk-affine and risk-averse investors.