Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

Niko Hauzenberger, Florian Huber, Massimiliano Marcellino and Nico Petz

Papers from arXiv.org

Abstract: We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroskedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is illustrated by means of simulated data and in a forecasting exercise with US data. Moreover, we use the GP-VAR to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.

Date: 2021-12, Revised 2022-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://arxiv.org/pdf/2112.01995 Latest version (application/pdf)

Related works:
Journal Article: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty (2025)
Working Paper: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty (2022)
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:arx:papers:2112.01995

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().