Markku Lanne, Jani Luoto, Adam Rybarczyk
arXiv 24 Apr 2026 · Econometrics
arXiv:2604.22445 · PDF · DOI · OpenAlex · Extracted main text
We propose a new approach to inference in tightly identified and large-scale structural vector autoregressions based on a reparameterization that enables imposing identifying inequality restrictions through continuously differentiable mappings. Permitted inequality restrictions include shape and ranking restrictions as well as bounds on economically relevant elasticities, and the approach is also able to accommodate zero restrictions in a straightforward manner. We implement a Hamiltonian Monte Carlo algorithm and show how the posterior density can be rapidly evaluated under the reparameterization, thus facilitating inference in high-dimensional settings. Two empirical applications demonstrate that our approach tends to result in lower serial dependence in Markov chains, larger effective sample sizes and reduced computation time relative to existing methods.
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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 | Arias, Jonas E and Rubio-Ramirez, Juan F and Shin, Minchul (2025) A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs | 1.000 | 21 | 5 | 100% |
| 2 | Chan, Joshua and Matthes, Christian and Yu, Xuewen (2025) Large structural VARs with multiple sign and ranking restrictions | 1.000 | 11 | 4 | 100% |
| 3 | Toru Kitagawa and Yizhou Kuang (2025) Identification-Aware Markov Chain Monte Carlo | 1.000 | 9 | 3 | 100% |
| 4 | Vehtari, Aki and Gelman, Andrew and Simpson, Daniel and Carpenter, B… (2021) Rank-Normalization, Folding, and Localization: An Improved $ R$ for Assessing Convergence of MCMC (with Discussion) | 1.000 | 5 | 3 | 100% |
| 5 | Arias, Jonas E. and Rubio-Ramírez, Juan F. and Waggoner, Daniel F (2018) INFERENCE BASED ON STRUCTURAL VECTOR AUTOREGRESSIONS IDENTIFIED WITH SIGN AND ZERO RESTRICTIONS: THEORY AND APPLICATIONS | 0.843 | 3 | 3 | 100% |
| 6 | Matthew Read and Dan Zhu (2025) Fast Posterior Sampling in Tightly Identified SVARs Using `Soft' Sign Restrictions | 0.843 | 3 | 3 | 100% |
| 7 | Stan Development Team (2024) Stan Reference Manual, 2.34 | 0.737 | 5 | 4 | 40% |
| 8 | Hoffman, Matthew D and Gelman, Andrew (2014) The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo | 0.737 | 3 | 3 | 67% |
| 9 | Vats, Dootika and Flegal, James M. and Jones, Galin L (2019) Multivariate Output Analysis for Markov Chain Monte Carlo | 0.737 | 3 | 2 | 100% |
| 10 | Kilian, Lutz and Murphy, Daniel P (2014) The Role of Inventories and Speculative Trading in the Global Market for Crude Oil | 0.693 | 6 | 1 | 100% |
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