Sign Restrictions in High-Dimensional Vector Autoregressions

This paper proposes a new Bayesian sampling scheme for VAR inference using sign restrictions. We build on a factor model decomposition of the reduced-form VAR disturbances, which are assumed to be driven by a few fundamental factors/shocks. The outcome is a computationally efficient algorithm that allows to jointly sample VAR parameters as well as decompositions of the covariance matrix satisfying desired sign restrictions. Using artificial and real data we show that the new algorithm works well and is multiple times more efficient than existing accept/reject algorithms for sign restrictions.

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