A Bayesian Gaussian Process Dynamic Factor Model

Tony Chernis, Niko Hauzenberger, Haroon Mumtaz and Michael Pfarrhofer

Papers from arXiv.org

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.

Date: 2025-09
New Economics Papers: this item is included in nep-dcm and nep-ets
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