Large hybrid time-varying parameter VARs

Joshua Chan

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few macroeconomic variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs - VARs with time-varying parameters in some equations but constant coefficients in others. Specifically, for each equation, the new method automatically decides (i) whether the VAR coefficients are constant or time-varying, and (ii) whether the error variance is constant or has a stochastic volatility specification. Using US datasets of various dimensions, we find evidence that the VAR coefficients and error variances in some, but not all, equations are time varying. These large hybrid TVP-VARs also forecast better than standard benchmarks.

Keywords: large vector autoregression; time-varying parameter; stochastic volatility; trend output growth; macroeconomic forecasting (search for similar items in EconPapers)
JEL-codes: C11 C52 C55 E37 E47 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2019-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Large Hybrid Time-Varying Parameter VARs (2023)
Working Paper: Large Hybrid Time-Varying Parameter VARs (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2019-77

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