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Discussion Paper Details

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Title: Structural Analysis with Multivariate Autoregressive Index Models

Author(s): Andrea Carriero, George Kapetanios and Massimiliano Marcellino

Publication Date: September 2015

Keyword(s): Bayesian VARs, factor models, forecasting, large datasets, Multivariate Autoregressive Index models, Reduced Rank Regressions and structural analysis

Programme Area(s): Monetary Economics and Fluctuations

Abstract: 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.

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Bibliographic Reference

Carriero, A, Kapetanios, G and Marcellino, M. 2015. 'Structural Analysis with Multivariate Autoregressive Index Models'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=10801