A Gibbs sampler for structural vector autoregressions

Structural VAR modeling has played an important role in empirical macroeconomics. The importance sampler used in the existing literature, however, can be prohibitively inefficient for obtaining accurate finite-sample inferences. In this paper we develop a Gibbs sampler for Bayesian inferences of str...

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Bibliographic Details
Published in:Journal of economic dynamics & control 2003-11, Vol.28 (2), p.349-366
Main Authors: Waggoner, Daniel F., Zha, Tao
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian method
Econometrics
Economic dynamics
Economic models
Economics
Gibbs sampler
Importance sampling
Macroeconomics
Mathematical methods
Regression analysis
Simultaneity
Studies
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Summary:Structural VAR modeling has played an important role in empirical macroeconomics. The importance sampler used in the existing literature, however, can be prohibitively inefficient for obtaining accurate finite-sample inferences. In this paper we develop a Gibbs sampler for Bayesian inferences of structural VARs that restrict the covariance matrix of reduced-form residuals. Our method is computationally efficient in comparison to the existing method and can be readily applied. We show, by examples, that inferences based on the importance sampler can seriously distort economic interpretations.
ISSN:0165-1889
1879-1743
DOI:10.1016/S0165-1889(02)00168-9