Large time-varying parameter VARs: a non-parametric approach

George Kapetanios, Massimiliano Marcellino and Fabrizio Venditti

No 1122, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: In this paper we introduce a non-parametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large (parametric) Bayesian VARs with time-varying parameters. The tool can also be used for structural analysis. As an example, we study the time-varying effects of oil price innovations on sectoral U.S. industrial output. We find that durable consumer goods and durable materials (which together account for slightly more than one fifth of total industrial output) play a key role in explaining the changing interaction between unexpected oil price increases and U.S. business cycle fluctuations.

Keywords: large VARs; time-varying parameters; non-parametric estimation; forecasting (search for similar items in EconPapers)
JEL-codes: C14 C32 C53 C55 (search for similar items in EconPapers)
Date: 2017-06
New Economics Papers: this item is included in nep-ets and nep-ore
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Related works:
Journal Article: Large time‐varying parameter VARs: A nonparametric approach (2019)
Working Paper: Large Time-Varying Parameter VARs: A Non-Parametric Approach (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1122_17

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