arXiv 28 Mar 2026 · Econometrics
arXiv:2603.27088 · PDF · DOI · OpenAlex · Extracted main text
We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially speeds up sampling when identification is tight. It also facilitates implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in an oil-market model identified using a rich set of sign, elasticity and narrative restrictions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Amir-Ahmadi, P. and T. Drautzburg (2021) Identification and Inference with Ranking Restrictions | 1.000 | 7 | 4 | 100% |
| 2 | Baumeister, C. and J. D. Hamilton (2024) Advances in Using Vector Autoregressions to Estimate Structural Magnitudes | 1.000 | 5 | 3 | 100% |
| 3 | Kilian, L. and D. P. Murphy (2012) Why Agnostic Sign Restrictions Are Not Enough: Understanding the Dynamics of Oil Market VAR Models | 1.000 | 5 | 3 | 100% |
| 4 | Giacomini, R. and T. Kitagawa (2021) Robust Bayesian Inference for Set-identified Models | 0.946 | 13 | 6 | 85% |
| 5 | Giacomini, R., T. Kitagawa, and M. Read (2022) Robust Bayesian Inference in Proxy SVARs | 0.928 | 4 | 3 | 100% |
| 6 | Neal, R. M (2003) Slice Sampling | 0.928 | 4 | 3 | 100% |
| 7 | Antolín-Díaz, J. and J. F. Rubio-Ramírez (2018) Narrative Sign Restrictions for SVARs | 0.894 | 7 | 4 | 71% |
| 8 | Montiel Olea, J. L. and J. Nesbit (2021) (Machine) Learning Parameter Regions | 0.874 | 6 | 3 | 67% |
| 9 | Baumeister, C. and J. D. Hamilton (2015) Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information | 0.843 | 4 | 3 | 75% |
| 10 | Giacomini, R., T. Kitagawa, and M. Read (2023) Identification and Inference under Narrative Restrictions, Reserve Bank of Australia Research Discussion Paper No 2023-07 | 0.843 | 4 | 3 | 75% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference in Tightly Identified and Large-Scale Sign-Restricted SVARs | 0.843 | 3 | 3 |