A Large non-Gaussian Structural VAR with Application to Monetary Policy

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ID: 320813
2026
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Abstract
Abstract We develop a large structural vector autoregression (SVAR) that is statistically identified using higher-order moments under non-Gaussian, mutually independent shocks, without imposing economically motivated restrictions. Estimation is performed via an efficient Gibbs sampler that scales well with dimensionality, and we introduce an estimator of the Deviance Information Criterion to facilitate model comparison, including the evaluation of over-identifying economic restrictions. The model handles high-dimensional systems through a factor structure in the reduced-form errors, allowing the number of structural shocks to be substantially smaller than the number of observed variables while explicitly accounting for idiosyncratic noise. Experiments with simulated data show that ignoring this noise can lead to biased estimates. An empirical application to U.S. monetary policy demonstrates that expanding the information set can materially affect structural inference.
Reference Key
openalex_W7168136952 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jan Prüser
Journal econometrics journal
Year 2026
DOI
10.1093/ectj/utag020
URL
https://doi.org/10.1093/ectj/utag020
Keywords Keywords not found

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