We assess the role that nontradable goods play as a determinant of fiscal spending multipliers, making use of a two-sector model. While fiscal multipliers increase with the share of nontradable goods, an inverted U-shaped relationship exists between multiplier size and the import share. Employing an interacted panel VAR model for EU countries, we estimate the effect of the share of nontradable goods on fiscal spending multipliers. Our empirical results provide strong evidence for the predictions of the theoretical model. They imply that the drag of fiscal consolidations is on average smaller in countries with a low share of nontradable goods.

We analyse the spatial relationships of economic output dynamics in European regions from 2000 to 2019 using dynamic spatial autoregressive growth models. In contrast to previous studies that rely on exogenous spatial weight matrices based solely on geographical proximity, we use a novel Bayesian approach to fully estimate the spatial weight matrix. Our results show that economic and sectoral characteristics (e.g., sectoral production structure, education, etc.) significantly influence the degree of regional interdependence. The approach thus allows to study the complex dynamics of regional economic development beyond mere distance.

We analyse the emergence of the Vienna Initiative (VI) as a public-private partnership in the wake of the global financial crisis and its short-term impact on risk metrics of Western European banks and individual countries. We find that adverse herding behaviour toward banks provides an explanation for banks' (non-)participation in the initiative. The VI measures were successful in mitigating adverse herding behaviour, underscoring their strong signalling effect on investor sentiment. Additionally, they attenuated financial market stress in those Central, Eastern and South-eastern European economies that were addressed by the VI, while having only minor adverse spillovers to those that were not.

We revisit the US weekly economic index (WEI) put forth by Lewis, Mertens, Stock and Trivedi (2021). In a narrow sense, we replicate their main results with data gathered from its original sources. In a wide sense, we apply the methodology established in Wegmüller, Glocker and Guggia (2023) to adjust the weekly input series for seasonal patterns, calendar day effects, and excess volatility. In a long sense, we show that our proposed data adjustment significantly improves the nowcasting performance of the WEI.

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long-time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables; and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators.

Die deutsche Wirtschaft leidet unter gewaltigen Angebotsschocks. Engpässe bei Energie, Vorprodukten und Arbeitskräften belasten die Produktion und treiben die Inflation auf Rekordhöhen. Der Staat versucht die Folgen mit breit angelegten Entlastungsprogrammen abzufedern. Er schafft damit aber auch Nachfrage, die bei beschränkten Produktionskapazitäten den Preisauftrieb hoch hält. Zwar dürfte als Folge der staatlichen Strom- und Gaspreisbremsen die Inflationsrate von 7,8% in diesem Jahr auf 6,4% im kommenden Jahr sinken. Gleichzeitig wird allerdings die Kernrate voraussichtlich von 4,8% auf 5,8% steigen. Erst im Jahr 2024 dürfte der Preisdruck langsam nachgeben und die Inflationsrate auf 2,8% bzw. die Kernrate auf 2,6% zurückgehen. Das Bruttoinlandsprodukt wird im Winterhalbjahr 2022/23 schrumpfen und die deutsche Wirtschaft damit in eine Rezession geraten. Ab dem Frühjahr 2023 dürfte sich die Konjunktur dann erholen und die Wirtschaft in der zweiten Jahreshälfte mit kräftigeren Raten zulegen, wenn die Einkommen wieder stärker steigen als die Preise. Alles in allem wird das Bruttoinlandsprodukt in diesem Jahr um 1,8% zunehmen und im kommenden Jahr geringfügig um 0,1% schrumpfen. Im Jahr 2024 liegt der Zuwachs dann wieder bei 1,6%.

We assess the effectiveness of the financial sector stabilisation measures taken by the Austrian authorities in the wake of the global financial crisis. Employing an event study methodology, we evaluate domestic and cross-border effects involving Central, Eastern and South-eastern European economies. We identify recapitalisations and public guarantees as the most effective sovereign interventions. Both mitigate financial market stress at home and abroad. However, a risk-shifting effect emerges at the sovereign's expense which undermines their effectiveness relative to monetary policy interventions. Moreover, in complement to the actual implementation, the mere announcement of interventions already mitigates financial market stress, underscoring the extent of policy credibility.

We study empirically how various labour market institutions – (i) union density, (ii) unemployment benefit remuneration, and (iii) employment protection – shape fiscal multipliers and output volatility. Our theoretical model highlights that more stringent labour market institutions attenuate both fiscal spending multipliers and macroeconomic volatility. This is validated empirically by an interacted panel vector autoregressive model estimated for 16 OECD countries. The strongest effects emanate from employment protection, followed by union density. While some labor market institutions mitigate the contemporaneous impact of shocks, they, however, reinforce their propagation mechanism. The main policy implication is that stringent labor market institutions render cyclical fiscal policies less relevant for macroeconomic stabilization.

We propose a modelling approach based on a set of small-scale factor models linked together in a cluster with linkages derived from Granger causality tests. GDP forecasts are produced using a disaggregated approach across production, expenditure and income accounts. The method combines the advantages of large structural macroeconomic models and small factor models, making our cluster of dynamic factor models (CDFM) useful for large-scale model-consistent forecasting. The CDFM has a simple structure, and its forecasts outperform those of a variety of competing models and professional forecasters. In addition, the CDFM allows forecasters to use their own judgment to produce conditional forecasts.

We present an uncertainty measure that is based on a business survey in which uncertainty is captured directly by a qualitative question on subjective uncertainty regarding expectations. Uncertainty perceptions display persistence at the firm level and changes are associated with past business assessments and expectations. While our uncertainty measure correlates with commonly used alternatives, it is superior in forecasting and suggests a larger role of uncertainty shocks for aggregate fluctuations. Its informational content is highest when considering smaller firms or firms with a low growth rate. Our results confirm the feasibility of constructing uncertainty measures from business survey questions that elicit information on uncertainty of respondents directly.