Large SVARs

[2505.23542] Large SVARs Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2505.23542 (econ) [Submitted on 29 May 2025 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Large SVARs Authors: Jonas E. Arias , Juan F. Rubio-Ramírez , Daniel Rudolf , Minchul Shin View a PDF of the paper titled Large SVARs, by Jonas E. Arias and 3 other authors View PDF HTML (experimental) Abstract: We develop a new algorithm for inference in structural vector autoregressions (SVARs) identified with sign restrictions that can accommodate big data and modern identification schemes. The key innovation of our approach is to move beyond the traditional accept-reject framework commonly used in sign-identified SVARs. We show that an elliptical slice within Gibbs sampler can deliver dramatic gains in computational speed and render previously infeasible applications tractable. We also prove that the algorithm is well-defined, in the sense that its stationary distribution coincides with the posterior distribution of interest. To illustrate the approach in the context of sign-identified SVARs, we use a tractable example. We further assess the performance of our algorithm through two applications: a well-known small-SVAR model of the oil market featuring a tight identified set, and a large SVAR model with more than ten shocks and 100 sign restrictions. Comments: 58 pages, 14 figures Subjects: Econometrics (econ.EM) ; Machine Learning (stat.ML) Cite as: arXiv:2505.23542 [econ.EM] (or arXiv:2505.23542v3 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2505.23542 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Minchul Shin [ view email ] [v1] Thu, 29 May 2025 15:21:10 UTC (900 KB) [v2] Fri, 30 May 2025 21:22:00 UTC (944 KB) [v3] Fri, 10 Apr 2026 07:02:48 UTC (1,869 KB) Full-text links: Access Paper: View a PDF of the paper titled Large SVARs, by Jonas E. Arias and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2025-05 Change to browse by: econ stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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