Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference

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 id...

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Bibliographic Details
Published in:The Review of economic studies 2010-04, Vol.77 (2), p.665-696
Main Authors: RUBIO-RAMÍREZ, JUAN F, WAGGONER, DANIEL F, ZHA, TAO
Format: Article
Language:English
Subjects:
Algorithms
Cointegration
Demand shocks
Econometric models
Econometrics
Economic models
Economic theory
Estimating techniques
Federal Reserve Bank
General economic equilibrium
Identification
Impulse response functions
Inference
Matrices
Monetary policy
Parametric models
Regression analysis
Stochastic models
Studies
Sufficient conditions
Time series
Unit root
Vector autoregression
Vector-autoregressive models
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Summary: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.
ISSN:0034-6527
1467-937X
DOI:10.1111/j.1467-937X.2009.00578.x