Machine learning: Optimising forecasting for better decision-making

Karin Klieber is a research economist in the Monetary Policy Section at the Austrian National Bank (Oesterreichische Nationalbank, OeNB). She specializes in the intersection of applied econometrics and central banking, bridging academic research and policy needs. Her work focuses on inflation, monetary policy, and machine learning in applied econometrics, and has been published in top field journals such as the Journal of Econometrics and the Journal of Applied Econometrics. She has recently been a visiting researcher at the Federal Reserve Bank of New York and has worked at the European Central Bank. She holds a PhD from the University of Salzburg.

Elvira Prades is a Research Economist in the Economic Analysis Division at the Bank of Spain (Banco de España, BdE) and an Adviser at the BIS Innovation Hub. Her research lies at the intersection of international trade, global value chains, and inflation and price setting dynamics, with a particular focus on the integration of machine learning methods into empirical economic analysis. She has held visiting research positions at the Banque de France and the Central Bank of Chile. She holds a PhD from the European University Institute (EUI).

Thomas Gottron is Principal Data Science Expert at the Data Office of the European Central Bank. He leads the activities on Artificial Intelligence, Advanced Analytics and Data Quality to support data science and machine learning projects in all business areas. Furthermore, he coordinates the ECB Machine Learning Community, which serves as platform to transfer knowledge and experience across more than 900 colleagues from all business areas at the ECB and other central banks.

Mr. Gottron has a background in mathematics and business management and received a PhD in computing science in 2008. He has more than 20 years of experience in designing and implementing data driven projects in academics, in the private and in the public sector. He authored more than 100 scientific publications on topics related to information retrieval, artificial intelligence, and data integration. His academic work has received several prices, including the Internet technologies and Applications best paper award 2009, winner of the Semantic Web Billion Triple Challenge in 2011 and the Web Science Test of Time Award 2026 as recognition for a paper that has stood the test of time through continued relevance and impact.

Carlos Montes-Galdón is an Adviser in the Directorate General of Economics at the European Central Bank (ECB), where he has been contributing since 2015. Over the years, he has played different roles in various divisions within the ECB. Initially, he worked in the Prices and Costs division, followed by the Forecasting and Policy Modelling division, where he focused on the development and refinement of macroeconomic models to inform policy decisions. In his current role, he serves in the Euro Area External Sector and Euro Adoption division, where his expertise supports analytical and modelling projects related to external trade and the euro area’s international position.

Before joining the ECB, he completed his Ph.D. in Economics at Columbia University in the City of New York. His research interests span macroeconomics and monetary policy, with a particular emphasis on the application of quantitative techniques, including Bayesian statistics and Deep Learning, to address complex economic questions.

In addition to his role at the ECB, he has contributed to the academic and policy-oriented economics community through research publications and collaborative projects. His work often bridges theoretical insights and practical applications, helping to inform policy debates on monetary policy and economic forecasting.