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Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

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  • Hauzenberger, Niko
  • Huber, Florian
  • Marcellino, Massimiliano
  • Petz, Nico

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.

Suggested Citation

  • Hauzenberger, Niko & Huber, Florian & Marcellino, Massimiliano & Petz, Nico, 2022. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," CEPR Discussion Papers 17646, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17646
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    Cited by:

    1. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org, revised Nov 2024.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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