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- Ribers (2)
- Ribers, Michael A. (2)
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- Ullrich, Hannes (2)
- Coulombe (1)
- Coulombe, Philippe Goulet (1)
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- Ellingsen, Jon (1)
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- Kalamara (1)
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- Kholodilin, Konstantin A. (1)
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- Kunaschk, Max (1)
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Resources: 10
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News media versus FRED‐MD for macroeconomic forecasting (replication data)
Ellingsen, Jon (Centre for Applied Macroeconomics and Commodity Prices BI Norwegian Business School Oslo Norway); Larsen, Vegard H. (Norges Bank and Centre for Applied Macroeconomics and Commodity Prices BI Norwegian Business School Oslo Norway); Thorsrud, Leif Anders (Centre for Applied Macroeconomics and Commodity Prices BI Norwegian Business School Oslo Norway)Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Fo...published 2022, Version 1Download -
Does model complexity add value to asset allocation? Evidence from machine learning forecasting models (replication data)
Kynigakis, Iason (Smurfit Graduate Business School University College Dublin Dublin Ireland); Panopoulou, Ekaterini (Essex Business School University of Essex Colchester UK)This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out-of-sample asset allocation framework. The economic evaluation of the forecasts shows that model complexity translates to improved r...published 2022, Version 1Download -
How is machine learning useful for macroeconomic forecasting? (replication data)
Coulombe, Philippe Goulet (Économie Publique, AgroParisTech, INRA; Université Paris-Saclay; Thiverval-Grignon France); Leroux, Maxime (Département des Sciences économiques Université du Québec à Montréal Montréal Québec Canada); Stevanovic, Dalibor (Département des Sciences économiques Université du Québec à Montréal Montréal Québec Canada); Surprenant, Stéphane (Département des Sciences économiques Université du Québec à Montréal Montréal Québec Canada)We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study the usefulness of the unde...published 2022, Version 1Download -
Making text count: Economic forecasting using newspaper text (replication data)
Kalamara, Eleni (Office for National Statistics Prev. Bank of England London England); Turrell, Arthur (International Monetary Fund Prev. Bank of England London England); Redl, Chris (King's College London London England); Kapetanios, George (Office for National Statistics Prev. Bank of England London England); Kapadia, Sujit (European Central Bank Prev. Bank of England London England)This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers tha...published 2022, Version 1Download -
Can Algorithms Reliably Predict Long-Term Unemployment in Times of Crisis? – Evidence from the COVID-19 Pandemic
Kunaschk, Max; Lang, JuliaIn this paper, we compare two popular statistical learning techniques, logistic regression and random forest, with respect to their ability to classify jobseekers by their likelihood to become long-term unemployed. We study the performance of the two methods before the COVID-1...published 2022-05-09, Version 1Download -
Machine Learning for Labour Market Matching
Mühlbauer, Sabrina; Weber, EnzoThis paper develops a large‑scale application to improve the labour market matching process with model‑ and algorithm‑based statistical methods. We use comprehensive administrative data on employment biographies covering individual and job‑related information ofworkers in Germ...published 2022-02-02, Version 1Download -
Künstliche Intelligenz und Daten können bei der Eindämmung von Antibiotikaresistenzen helfen
Ribers, Michael A.; Ullrich, HannesEine zentrale Strategie, um der Zunahme von Antibiotikaresistenzen entgegenzutreten, ist die Verbesserung der ärztlichen Verschreibungspraxis. Damit sollen Fehlverschreibungen von Antibiotika als eine Hauptursache von Antibiotikaresistenzen vermieden werden. Die zunehmende Ver...published 2019, Version 2.0Download -
Cyber-physische Produktion: Modelle und Inszenierung der Smart Factory
Ionescu, Tudor; Merz, MartinaDer Artikel rekonstruiert die Entwicklung eines Smart Factory-Demonstrators in einem Großkonzern. Er zeigt auf, wie den dabei auftretenden Herausforderungen in der Praxis begegnet und eine bestimmte Vision der intelligenten Fabrik in Demonstrationen für ein Expertenpublikum in...published 2018, Version 1.0.0Download -
Artificial Intelligence and Big Data Can Help Contain Resistance to Antibiotics
Ribers, Michael A.; Ullrich, HannesImproving physicians’ prescription practices is a primary strategy for countering the rise in resistance to antibiotics. This would prevent physicians from incorrectly prescribing antibiotics, one of the main causes of antibiotic resistance. The increasing availability of medi...published 2019, Version 2.0Download -
High Risk of a Housing Bubble in Germany and Most OECD Countries
Kholodilin, Konstantin A.; Michelsen, ClausHousing prices in many countries have increased significantly over the past years, fueling a fear that speculative price bubbles will return. However, it can be difficult for policymakers to recognize when regulatory interventions in the market are necessary to counteract bubb...published 2019, Version 2.0Download
Number of resources: 10