Discussion paper

DP11560 Large Time-Varying Parameter VARs: A Non-Parametric Approach

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.

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Citation

Marcellino, M, G Kapetanios and F Venditti (2016), ‘DP11560 Large Time-Varying Parameter VARs: A Non-Parametric Approach‘, CEPR Discussion Paper No. 11560. CEPR Press, Paris & London. https://cepr.org/publications/dp11560