Large Vector Autoregressions with Asymmetric Priors

Andrea Carriero, Todd Clark and Massimiliano Marcellino

No 759, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to simulate the conditional mean coefficients of the VAR by drawing them equation by equation. This strategy reduces the computational complexity by a factor of N2 with respect to the existing algorithms routinely used in the literature and by practitioners. Importantly, this new algorithm can be easily obtained by modifying just one of the steps of the existing algorithms. We illustrate the benefits of the algorithm with numerical and empirical applications.

Keywords: Bayesian VARs; Stochastic volatility; Large datasets; Forecasting; Impulse response functions (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
Date: 2015-11-28
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2015/items/wp759.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:759

Access Statistics for this paper

More papers in Working Papers from Queen Mary University of London, School of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Nicholas Owen ( this e-mail address is bad, please contact ).