Das Produktionspotenzial der deutschen Wirtschaft wächst mittelfristig (2023 bis 2029) mit einer jahresdurchschnittlichen Rate von 0,3% und damit deutlich schwächer als in den Jahren zuvor. Dies ist auf eine ungünstigere Entwicklung aller drei Faktoren (Arbeitsvolumen, Kapitalstock, totale Faktorproduktivität) zurückzuführen. Das potenzielle Wachstum wird insbesondere durch den Rückgang der durchschnittlichen Arbeitszeit gedämpft.

Our results reveal that higher lending spreads between foreign and home markets redirect real estate backed lending towards foreign markets offering a higher interest rate, which provides evidence for "search for yield" behavior. This re-allocation is found especially for banks with more expertise on the foreign market due to a higher local activity and holds for commercial and residential real estate backed loans. Furthermore, "search for yield" behavior and a resulting increase in foreign real estate backed lending is found when macroprudential regulation is missing or misaligned between a bank’s country of residence and the destination country. When turning to the question of whether the detected search for yield behavior results in more risk, we find that especially better capitalized banks report higher forbearance ratios as they might face less stigma effects compared to low capitalized banks.

This paper investigates the critical role of church membership in the process of economic recovery after high-impact natural disasters. We document a significant adverse treatment effect of the 2005 hurricane season in the Southeastern United States on establishment-level productivity. However, we find that establishments in counties with higher rates of church membership saw a significantly stronger recovery in terms of productivity for 2005–10. We also show that church membership is correlated with post-disaster entrepreneurship activities and population growth.

This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.