Large Time-Varying Parameter VARs: A Non-Parametric Approach

Massimiliano Marcellino, George Kapetanios and Fabrizio Venditti

No 11560, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: In this paper we introduce a nonparametric 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 the changing interaction between unexpected oil price increases and business cycle fluctuations is shaped by the durable materials sector, rather by the automotive sector on which a large part of the literature has typically focused.

Date: 2016-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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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 (2017)
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