Sicco Kooiker will present his latest research, together with co-authors Janneke van Brummelen, Julia Schaumburg and Marcin Zamojski, at the 13th Conference on Forecasting Techniques hosted by the European Central Bank on 23–24 March 2026 in Frankfurt am Main.
The theme of the conference is “Artificial intelligence in the analysis of economic narratives, forecasting, and risk assessment.” In their paper, Sicco and his co-authors propose a self-driving neural network factor model for modeling yield curves. Their method offers a flexible and adaptive, yet interpretable, approach with better forecasting performance than static and less flexible models.
This research focuses on developing an alternative to the well-known Nelson-Siegel model, a three-factor model for the yield curve. In their paper, the authors model the factor loadings using neural networks with observation-driven parameters. Through carefully designed constraints, the output factors of this self-driving neural network factor model remain interpretable as the level, slope and curvature of the yield curve. The neural network and the self-driving dynamics lead to better forecast performance for U.S. government bond yields at horizons of one to twelve months ahead, relative to a variety of benchmark models, including the Nelson-Siegel model.
At the upcoming conference, Sicco will present these findings in Session 5 at 14:30: Time-Variation and State Dependence, a session dedicated to how research on time-varying models can improve economic and financial forecasting. Their contribution stands out for introducing novel time series machine-learning techniques to enhance yield curve forecasting.
The ECB Conference on Forecasting Techniques is one of Europe’s leading platforms for advances in economic forecasting, with a strong focus this year on the transformative role of artificial intelligence in economic analysis. A livestream, working paper and presentation slides will become available at the following link:
https://www.ecb.europa.eu/press/conferences/html/20260323_13th_conference_on_forecasting_techniques.en.html