A Bayesian Gaussian Process Dynamic Factor Model - Niko Hauzenberger (University of Strathclyde)
Wednesday 3 December 2025, 1:30pm to 2:30pm
Venue
CHC - Charles Carter A15 - View MapOpen to
All Lancaster University (non-partner) students, Postgraduates, Staff, UndergraduatesRegistration
Registration not required - just turn upEvent Details
Economics Research Seminar
Abstract: We propose a dynamic factor model (DFM) where the latent factors are linked to observed variables with unknown and potentially nonlinear functions. The key novelty and source of flexibility of our approach is a nonparametric observation equation, specified via Gaussian Process (GP) priors for each series. Factor dynamics are modeled with a standard vector autoregression (VAR), which facilitates computation and interpretation. We discuss a computationally efficient estimation algorithm and consider two empirical applications. First, we forecast key series from the FRED-QD dataset and show that the model yields improvements in predictive accuracy relative to linear benchmarks. Second, we extract driving factors of global inflation dynamics with the GP-DFM, which allows for capturing international asymmetries.
Speaker
University of Strathclyde
Research interests in developing novel econometric methods for the efficient use of big data in macroeconomics.
Contact Details
Name | Stefano Fasani |