Die deutsche Wirtschaft tritt seit über zwei Jahren auf der Stelle. In den kommenden Quartalen dürfte eine langsame Erholung einsetzen. Aber an den Trend von vor der COVID-19-Pandemie wird das Wirtschaftswachstum auf absehbare Zeit nicht mehr anknüpfen können. Die Dekarbonisierung, die Digitalisierung, der demografische Wandel und wohl auch der stärkere Wettbewerb mit Unternehmen aus China haben strukturelle Anpassungsprozesse in Deutschland ausgelöst, die die Wachstumsaussichten für die deutsche Wirtschaft dämpfen. 

Das Bruttoinlandsprodukt dürfte im Jahr 2024 um 0,1% sinken und in den kommenden beiden Jahren um 0,8% bzw. 1,3% zunehmen. Damit revidieren die Institute ihre Prognose vom Frühjahr 2024 leicht nach unten. Getragen wird die schmalspurige Erholung vom steigenden privaten Verbrauch, der von kräftigen Zuwächsen der real verfügbaren Einkommen angeregt wird. Das Anziehen der Konjunktur in wichtigen Absatzmärkten, wie den europäischen Nachbarländern, wird den deutschen Außenhandel stützen. Zusammen mit günstigeren Finanzierungsbedingungen kommt dies den Anlageinvestitionen zugute. Die Wirtschaftspolitik sollte Produktivitätshemmnisse abbauen, den Strukturwandel zulassen und die politische Unsicherheit verringern.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.

This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.

Using proprietary data from AngelList Talent, we study how startup job seekers’ search and application behavior changed during the COVID-19 downturn. We find that workers shifted their searches and applications away from less-established startups and toward more-established ones, even within the same individual over time. At the firm level, this shift was not offset by an influx of new job seekers. Less-established startups experienced a relative decline in the quantity and quality of applications, ultimately affecting their hiring. Our findings uncover a flight-to-safety channel in the labor market that may amplify the procyclical nature of entrepreneurial activities.