A Bayesian Gaussian Process Dynamic Factor Model
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909772454821888 |
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| author | Chernis, Tony Hauzenberger, Niko Mumtaz, Haroon Pfarrhofer, Michael |
| author_facet | Chernis, Tony Hauzenberger, Niko Mumtaz, Haroon Pfarrhofer, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Bayesian Gaussian Process Dynamic Factor Model Chernis, Tony Hauzenberger, Niko Mumtaz, Haroon Pfarrhofer, Michael Econometrics 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. |
| title | A Bayesian Gaussian Process Dynamic Factor Model |
| topic | Econometrics |
| url | https://arxiv.org/abs/2509.04928 |