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 Map

Open to

All Lancaster University (non-partner) students, Postgraduates, Staff, Undergraduates

Registration

Registration not required - just turn up

Event 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

Niko Hauzenberger

University of Strathclyde

Research interests in developing novel econometric methods for the efficient use of big data in macroeconomics.

Contact Details

Name Stefano Fasani
Email

s.fasani@lancaster.ac.uk

Directions to CHC - Charles Carter A15

Charles Carter building, room A15