Niko Hauzenberger, Florian Huber, Massimiliano Marcellino, Nico Petz
arXiv 3 Dec 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 16 citations (OpenAlex)
arXiv:2112.01995 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jurado, Ludvigson, and Ng (2015) Measuring uncertainty | 0.855 | 8 | 6 | 62% |
| 2 | Bloom (2014) Fluctuations in uncertainty | 0.644 | 2 | 2 | 100% |
| 3 | Chan (2021) Minnesota-type adaptive hierarchical priors for large Bayesian VARs | 0.644 | 2 | 2 | 100% |
| 4 | Chipman, George, and McCulloch (2010) BART: Bayesian additive regression trees | 0.644 | 2 | 2 | 100% |
| 5 | Crawford, Flaxman, Runcie, and West (2019) Variable prioritization in nonlinear black box methods: A genetic association case study | 0.644 | 2 | 2 | 100% |
| 6 | Kalli and Griffin (2018) Bayesian nonparametric vector autoregressive models | 0.644 | 2 | 2 | 100% |
| 7 | Koop (2013) Forecasting with medium and large Bayesian VARs | 0.644 | 2 | 2 | 100% |
| 8 | Primiceri (2005) Time varying structural vector autoregressions and monetary policy | 0.644 | 2 | 2 | 100% |
| 9 | Williams and Rasmussen (2006) Gaussian processes for machine learning, 2: MIT press Cambridge, MA | 0.585 | 3 | 1 | 100% |
| 10 | Chan (2017) The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling | 0.511 | 3 | 2 | 33% |
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