Quantile VARs and Macroeconomic Risk Forecasting

Stéphane Surprenant

Staff Working Papers from Bank of Canada

Abstract: Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112 monthly US variables over 40 years, with horizons of 1 to 12 months. We compare QVAR with three parametric benchmarks: a Gaussian VAR, a generalized autoregressive conditional heteroskedasticity VAR and a VAR with stochastic volatility. QVAR frequently, significantly and quantitatively improves upon the benchmarks and almost never performs significantly worse. Forecasting improvements are concentrated in the labour market and interest and exchange rates. Augmenting the QVAR model with factors estimated by principal components or quantile factors significantly enhances macroeconomic risk forecasting in some cases, mostly in the labour market. Generally, QVAR and the augmented models perform equally well. We conclude that both are adequate tools for modeling macroeconomic risks.

Keywords: Econometrics and statistical methods; Business fluctuations and cycles (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2025-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:25-4

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