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).

Olivier Sirello joined the BIS in June 2022. His areas of expertise are central bank statistics, balance of payments and securities statistics. He is also involved in a number of international initiatives related to official statistics, including the G20 Data Gaps Initiative, the High-Level Group for the Modernisation of Official Statistics of the United Nations Economic Commission for Europe (UNECE) and SDMX. He previously worked as an economist-statistician at the Bank of France and held post-graduate teaching positions at Sciences Po Paris. He studied at Sciences Po Paris, Princeton University and Bocconi University. He holds two MScs in economics and public policy.

Xin Zhang is an Advisor in the Research Division of Sveriges Riksbank. His research focuses on leveraging AI, machine learning, and econometric methodologies to tackle complex issues across monetary policy, financial stability, and financial market infrastructure. With a track record of publications in leading academic journals and impactful policy reports, his work bridges the gap between cutting-edge AI research and real-world financial regulation. Xin holds a PhD in Finance from VU University Amsterdam and Tinbergen Institute.