Gaussian process VARs and macroeconomic uncertainty. doi

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.

Publication (open access).

CEPR discussion paper.

arXiv.

Slides. Presented at CFE 2022, Decision Making under Uncertainty (DeMUr) 2022 workshop, EABCN and Bundesbank conference on challenges in empirical macroeconomics since 2020 (by co-author), ESOBE 2022 (by co-author), IAAE 2022, University of Montreal econometrics seminar (by co-author).