Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

Niko Hauzenberger, Florian Huber, Massimiliano Marcellino and Nico Petz

Journal of Business & Economic Statistics, 2025, vol. 43, issue 1, 27-43

Abstract: We develop a nonparametric 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 heteroscedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.

Date: 2025
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Working Paper: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty (2022)
Working Paper: Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:43:y:2025:i:1:p:27-43

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DOI: 10.1080/07350015.2024.2322089

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