Direct Gaussian Process Predictive Regressions with Mixed Frequency Data

Niko Hauzenberger, Massimiliano Marcellino, Michael Pfarrhofer and Anna Stelzer

No 21214, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We develop Bayesian machine learning methods for mixed frequency data. This involves handling frequency mismatches and specifying functional relationships between (possibly many) predictors and low frequency dependent variables. We use Gaussian Processes (GPs) in direct nonlinear predictive regressions, and compress higher frequency variables in a structured way. This yields a set of kernels for GPs with distinct properties and implications. We evaluate the proposed framework in an out-of-sample exercise focusing on quarterly US GDP growth and inflation. Our approach leverages high-dimensional mixed frequency data in a computationally efficient way, and offers robustness and gains in predictive accuracy along several dimensions.

JEL-codes: C11 C22 C53 E31 E37 (search for similar items in EconPapers)
Date: 2026-02
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP21214 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:21214

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP21214

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().