Tony Chernis, Niko Hauzenberger, Haroon Mumtaz, Michael Pfarrhofer
arXiv 5 Sep 2025 · Econometrics
arXiv:2509.04928 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCracken MW, and Ng S (2021) FRED-QD: A Quarterly Database for Macroeconomic Research | 0.928 | 4 | 3 | 100% |
| 2 | Svensson A, Solin A, Särkkä S, and Schön T (2016) Computationally efficient Bayesian learning of Gaussian process state space models, in Artificial Intelligence and Statistics, 2… | 0.843 | 4 | 3 | 75% |
| 3 | Lindsten F, Jordan MI, and Schön TB (2014) Particle Gibbs with Ancestor Sampling | 0.822 | 9 | 3 | 56% |
| 4 | Guerrón-Quintana P, Khazanov A, and Zhong M (2023) Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor Model | 0.811 | 4 | 2 | 100% |
| 5 | Frigola R, Lindsten F, Schön TB, and Rasmussen CE (2013) Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC, in | 0.737 | 3 | 2 | 100% |
| 6 | Hauzenberger N, Huber F, Marcellino M, and Petz N (2025) Gaussian process vector autoregressions and macroeconomic uncertainty | 0.737 | 3 | 2 | 100% |
| 7 | Solin A, and Särkkä S (2020) Hilbert space methods for reduced-rank Gaussian process regression | 0.737 | 3 | 2 | 100% |
| 8 | Williams CK, and Rasmussen CE (2006) Gaussian processes for machine learning | 0.737 | 3 | 2 | 100% |
| 9 | Andrieu C, Doucet A, and Holenstein R (2010) Particle Markov chain Monte Carlo methods | 0.644 | 4 | 2 | 50% |
| 10 | Diebold FX, and Mariano RS (1995) Comparing predictive accuracy | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 91 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonlinear Dynamic Factor Analysis With a Transformer Network | 0.405 | 1 | 1 |
| 2 | A Dynamic Factor Model for Level and Volatility | 0.405 | 1 | 1 |