Using a novel rich dataset at the regional level, this paper provides new empirical evidence on the €fiscal transmission mechanism in the Eurozone. Our baseline estimates reveal a government spending relative output multiplier of 2.9, an employment multiplier of 1.9, and a cost per job...

Als ein Teilbereich der Künstlichen Intelligenz stellt Machine Learning (ML) eine Schüsseltechnologie des 21. Jahrhunderts dar. Unter Nutzung der mathematisch-statistischen Verfahren sind technische Systeme realisierbar, die anhand von Daten eigenständig empirische Zusammenhänge erkennen und...

Machine Learning ist ein Forschungsfeld mit großen Potenzialen und weitreichenden Anwendungspotenzialen. Big Data kann dabei als Enabler angesehen werden, da große und qualitativ hochwertige Daten stets die Grundlage für erfolgreiche Machine Learning-Algorithmen und -Modelle darstellen....

Understanding behavioral aspects of collective decision-making is an important challenge for eco-nomics, and narratives are a crucial group-based mechanism that influences human decision-making. This paper introduces the Character-Role Narrative Framework as a tool to systematically analyze...

This work aimed to reproduce the methodology of Carl Benedikt Frey and Michael Osborne of 2017 for estimating the automation probabilities of occupations in Brazil. These estimates are potentially important for professionals and policymakers because they can guide the career of a worker, as well...

This research highlights the role played by the media in the inflation expectations formation process of different types of respondents in Ukraine. Using a large news corpus and machine learning techniques I constructed news-based measures transforming text into quantitative indicators, which...

We show that the news is a rich source of data on distressed firm links that drive firm- level and aggregate risks. The news tends to report about links in which a less popular firm is distressed and may contaminate a more popular firm. This constitutes a contagion channel that yields...

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that...