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.

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.

This paper examines the effects of the minimum wage introduction in Germany in 2015 on the skilled crafts sector in Saxony-Anhalt. Using novel survey data on the skilled crafts sector in Saxony-Anhalt, we examine three questions: (1) How many employees are affected by the minimum wage introduction in the skilled crafts sector in Saxony- Anhalt? (2) What are the effects of the minimum wage introduction? (3) How have firms reacted to wage increase? We find that about 8% of all employees in the skilled crafts sector in Saxony-Anhalt are directly affected by the minimum wage introduction. A difference-in-difference estimation reveals no significant employment effects of the minimum wage introduction. We test for alternative adjustment strategies and observe a significant increase of output prices.

This paper analyzes changes in the monetary policy in the Czech Republic, Hungary, and Poland following the policy shift from exchange rate targeting to inflation targeting around the turn of the millennium. Applying a Markovswitching dynamic stochastic general equilibrium model, switches in the policy parameters and the volatilities of shocks hitting the economies are estimated and quantified. Results indicate the presence of regimes of weak and strong responses of the central banks to exchange rate movements as well as periods of high and low volatility. Whereas all three economies switched to a less volatile regime over time, findings on changes in the policy parameters reveal a lower reaction to exchange rate movements in the Czech Republic and Poland, but an increased attention to it in Hungary. Simulations for the Czech Republic and Poland also suggest their respective central banks, rather than a sound macroeconomic environment, being accountable for reducing volatility in variables like inflation and output. In Hungary, their favorable developments can be attributed to a larger extent to the reduction in the size of external disturbances.

Im Mittelpunkt der gegenwärtigen ökonomischen Entwicklung steht verstärkt Wissen als fundamentale Ressource und Triebkraft wirtschaftlichen Wachstums sowie Lernen als der bedeutendste Prozess. Diese Sichtweise wird durch das theoretische Konstrukt der Learning Economy beschrieben. Die Organisationsform des Netzwerkes gilt dabei als geeignetes Arrangement, um die Teilnahme an Lernprozessen sowie den Zugangs zu einer breiten und diversifizierten Wissensbasis zu gewährleisten. In der vorliegenden Arbeit werden im Rahmen der konzeptionellen Charakteristika der Learning Economy die möglichen Ausprägungen einer aktiven Netzwerktätigkeit, die erfolgsdeterminierenden Mechanismen eines Netzwerkes als auch die mit einem Netzwerkarrangement verbundenen Probleme dargelegt. Es zeigt sich einerseits, dass Netzwerke grundsätzlich einen geeigneten Mechanismus darstellen, um den Notwendigkeiten einer Learning Economy zu begegnen. Andererseits wird ersichtlich, dass die Funktionsfähigkeit von Netzwerken und das Ausschöpfen der vorhandenen Möglichkeiten keinesfalls ohne konstante Anstrengungen der Partner und ein detailliertes Verständnis der zentralen Wirkungszusammenhänge erreicht werden kann. Daher ist die zum Teil euphorisch praktizierte Netzwerkdiskussion in mancher Hinsicht differenziert zu betrachten. Besonders der Abfluss ökonomisch sensiblen Wissens, der nicht-wechselseitige Wissensaustausch sowie die Abschottung bestimmter regionaler Netzwerkstrukturen können schwerwiegende Folgen nach sich ziehen.