Structural Analysis with Multivariate Autoregressive Index Models
George Kapetanios and
No 10801, CEPR Discussion Papers from C.E.P.R. Discussion Papers
We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely related to that associated with dynamic factor models. Next, we describe classical and Bayesian estimation of large MAIs, and discuss methods for the rank determination. Then, the theoretical analysis is extended to the case of general rank restrictions on the VAR coefficients. Finally, the performance of the MAIs is compared with that of large Bayesian VARs in the context of Monte Carlo simulations and two empirical applications, on on the transmission mechanism of monetary policy and the propagation of demand, supply and financial shocks.
Keywords: Bayesian VARs; factor models; forecasting; large datasets; Multivariate Autoregressive Index models; Reduced Rank Regressions; structural analysis (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Structural analysis with Multivariate Autoregressive Index models (2016)
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