Gaussian process vector autoregressions and macroeconomic uncertainty
Hauzenberger, Niko and Huber, Florian and Marcellino, Massimiliano and Petz, Nico (2024) Gaussian process vector autoregressions and macroeconomic uncertainty. Journal of Business and Economic Statistics. pp. 1-17. ISSN 0735-0015 (https://doi.org/10.1080/07350015.2024.2322089)
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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.
ORCID iDs
Hauzenberger, Niko ORCID: https://orcid.org/0000-0002-2683-8421, Huber, Florian, Marcellino, Massimiliano and Petz, Nico;-
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Item type: Article ID code: 88258 Dates: DateEvent29 March 2024Published29 March 2024Published Online7 February 2024AcceptedSubjects: Social Sciences > Economic Theory Department: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 27 Feb 2024 10:16 Last modified: 12 Dec 2024 15:19 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/88258