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Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference

RUBIO-RAMÍREZ, JUAN F., WAGGONER, DANIEL F., ZHA, TAO·2010·Review of Economic Studies
DOI 10.1111/j.1467-937x.2009.00578.x
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Abstract

Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic stochastic general equilibrium (DSGE) models; yet no workable rank conditions to ascertain whether an SVAR is globally identified have been established. Moreover, when nonlinear identifying restrictions are used, no efficient algorithms exist for small-sample estimation and inference. This paper makes four contributions towards filling these important gaps in the literature. First, we establish general rank conditions for global identification of both identified and exactly identified models. These rank conditions are sufficient for general identification and are necessary and sufficient for exact identification. Second, we show that these conditions can be easily implemented and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we show that the rank condition for exactly identified models amounts to a straightforward counting exercise. Fourth, we develop efficient algorithms for small-sample estimation and inference, especially for SVARs with nonlinear restrictions.

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Metadata

Authors
RUBIO-RAMÍREZ, J. F., WAGGONER, D. F., & ZHA, T.
Venue
Review of Economic Studies
Publisher
Oxford University Press (OUP)
Published
2010-04
Volume
77
Issue
2
Pages
665-696
ISSN
  • 0034-6527 (ISSN-L)
  • 1467-937X
DOI
10.1111/j.1467-937x.2009.00578.x
OpenAlex
W2109425163
Cited by
901
References
1
FWCI
45.54
Type
journal article
Language
en
Open access
closed

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RUBIO-RAMÍREZ, J. F., WAGGONER, D. F., & ZHA, T. (2010). Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference. Review of Economic Studies. https://doi.org/10.1111/j.1467-937x.2009.00578.x

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