Diese Studie untersucht, wie die betrieblichen Erträge zwischen deutschen Industrieunternehmen und ihren Beschäftigten aufgeteilt werden. Dafür werden Energiepreisänderungen auf Unternehmensebene und die daraus resultierenden Veränderungen im Unternehmensertrag betrachtet. Wir finden heraus, dass höhere Energiepreise die Löhne drücken und dass ein Rückgang bei den Erträgen um 10% zu einem Rückgang der Löhne um 2% führt. Dieser Zusammenhang ist asymmetrisch, was bedeutet, dass die Löhne nicht von Senkungen der Energiepreise profitieren, aber durch Energiepreiserhöhungen sinken. Kleine Unternehmen geben Schwankungen im Ertrag stärker an die Beschäftigten weiter als Großunternehmen.

Fußball-Welt- und Europameisterschaften sind große gesellschaftliche Ereignisse. Gesamtwirtschaftlich fallen sie in großen Volkswirtschaften mit bestehender Sport- und Verkehrsinfrastruktur und vorhandenen Kapazitäten im Gastgewerbe allerdings kaum ins Gewicht. In diesem Beitrag werden Studien zu den ökonomischen Effekten von Sportgroßveranstaltungen zusammengefasst, die wirtschaftlichen Effekte der Fußball-Weltmeisterschaft 2006 in Deutschland untersucht und daraus die zu erwartenden gesamtwirtschaftlichen Effekte der Fußball-Europameisterschaft 2024 in Deutschland abgeleitet. Die Bauaktivität in den Spielorten nimmt im Vorfeld der Meisterschaften zu; Effekte auf das Gastgewerbe gibt es hingegen kaum – vermutlich vor allem aufgrund von Verdrängungseffekten. Insgesamt war die gesamte nominale Bruttowertschöpfung im WM-Jahr 2006 in den Spielorten gut 1% höher als ohne die WM zu erwarten gewesen wäre; in realer Rechnung ist insgesamt kein signifikanter Effekt zu beobachten.

This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.

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.

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.

We investigate whether a firm’s exposure to climate change, as proxied by disclosures during quarterly earnings conference calls, provides forward-looking information to investors regarding the long-term association of stock prices with current earnings and the book values of equity. Following a key regulatory mandate around the formation of the cap-and-trade program to reduce emissions related to climate change, firms’ climate change exposure decreases the association between current earnings and stock prices while increasing the relevance of book values of equity (i.e., historical earnings). However, these relationships flip when the sentiment around climate change exposure is negative, suggesting that the risks related to climate change exposure provide forward-looking information to investors when they evaluate the ability of current earnings to predict firm values. Such a relationship is stronger for new economy firms and is sensitive to conservative accounting. We also observe that the inclusion of climate change disclosure to our models improves the joint ability of earnings and book values to predict stock prices.

Establishment closures have lasting negative consequences for the workers displaced from their jobs. We study how these consequences vary with the amount of skill mismatch that workers experience after job displacement. Developing new measures of occupational skill redundancy and skill shortage, we analyze the work histories of individuals in Germany between 1975 and 2010. We estimate difference-in-differences models, using a sample of displaced workers who are matched to statistically similar non-displaced workers. We find that displacements increase the probability of occupation change eleven-fold. Moreover, the magnitude of post-displacement earnings losses strongly depends on the type of skill mismatch that workers experience in such job switches. Whereas skill shortages are associated with relatively quick returns to the earnings trajectories that displaced workers would have experienced absent displacement, skill redundancy sets displaced workers on paths with permanently lower earnings. We show that these differences can be attributed to differences in mismatch after displacement, and not to intrinsic differences between workers making different post-displacement career choices.

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.

I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.

Climate change and inequality are critical and interrelated defining issues for this century. Despite growing empirical evidence on the economic incidence of climate policies and impacts, mainstream model-based assessments are often silent on the interplay between climate change and economic inequality. For example, all the major model comparisons reviewed in IPCC neglect within-country inequalities. Here we fill this gap by presenting a model ensemble of eight large-scale Integrated Assessment Models belonging to different model paradigms and featuring economic heterogeneity. We study the distributional implications of Paris-aligned climate target of 1.5 degree and include different carbon revenue redistribution schemes. Moreover, we account for the economic inequalities resulting from residual and avoided climate impacts. We find that price-based climate policies without compensatory measures increase economic inequality in most countries and across models. However, revenue redistribution through equal per-capita transfers can offset this effect, leading to on average decrease in the Gini index by almost two points. When climate benefits are included, inequality is further reduced, but only in the long term. Around mid-century, the combination of dried-up carbon revenues and yet limited climate benefits leads to higher inequality under the Paris target than in the Reference scenario, indicating the need for further policy measures in the medium term.