pith:43OUQYTK
Inference in Tightly Identified and Large-Scale Sign-Restricted SVARs
A differentiable reparameterization turns inequality restrictions into smooth constraints for efficient Hamiltonian Monte Carlo sampling in large-scale sign-restricted SVARs.
arxiv:2604.22445 v2 · 2026-04-24 · econ.EM
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Claims
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.
The continuously differentiable mappings used to impose shape, ranking, and elasticity restrictions correctly represent the original identifying constraints and preserve the target posterior distribution without introducing bias or numerical instabilities in high dimensions.
A differentiable reparameterization combined with HMC sampling improves posterior exploration and reduces computation time for tightly identified large-scale sign-restricted SVARs.
Receipt and verification
| First computed | 2026-05-22T01:04:03.047523Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e6dd48626a93d7ca8665a83369a32e8de90e186e1f4526551da30a546eeee992
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/43OUQYTKSPL4VBTFVAZWTIZORX \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e6dd48626a93d7ca8665a83369a32e8de90e186e1f4526551da30a546eeee992
Canonical record JSON
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