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Paper Citation Record · LEDGER

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions

As of 23 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.17275.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.17275 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:37:45.391395Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

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  • verified fuzzy0
  • unresolved14
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  • malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7052f599-3d61-4290-bef0-cf093e9ed780 · outbound

This paper cites and Stegun, I.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions and Stegun, I

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:37:44.620649Z digest=sha256:a85a06af1789f13305d0162c85ec67b0cd5e03ea9fcb73195de055445778bd86

Observation 8630c2c1-3a75-4955-acdb-4fe97b8f3c57 · outbound

This paper cites Condition (MBB).

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Condition (MBB)

Reference 2

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T18:37:45.157311Z digest=sha256:9a9b423ae8a37f08bf70913c42e958486c1da21261e08f9da21ee0d1e62dd8d5

Observation fcc3fbed-1b1e-45b7-8798-5aeaed1f68fd · outbound

This paper cites 24We fix the block length in the residual-based MBB algorithm to the largest integer smaller than 5.03T 1/4, see Hall et al.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions 24We fix the block length in the residual-based MBB algorithm to the largest integer smaller than 5.03T 1/4, see Hall et al

Reference 3

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Observation e23f7f2e-44a3-4fa5-a120-f399d27dc554 · outbound

This paper cites Since the true parameter lies at the boundary αk =∞, there is noO(α −1 k ) remainder (the leading departure from invariance isO(α −2 k ) and vanishes at first order).

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Since the true parameter lies at the boundary αk =∞, there is noO(α −1 k ) remainder (the leading departure from invariance isO(α −2 k ) and vanishes at first order)

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:37:44.781878Z digest=sha256:649a8fc2183cd70780e227da2e2965e882ee220def1cdc0d12879f9b379c844d

Observation 67e6bbbe-ed67-489a-9cce-0edc3e4cdf64 · outbound

This paper cites (1) Atρ k = 0, the NIG density departs from the Gaussian limit case at orderρ 2 k (its excess kurtosis is 3ρ 2 k/σ2 k).

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions (1) Atρ k = 0, the NIG density departs from the Gaussian limit case at orderρ 2 k (its excess kurtosis is 3ρ 2 k/σ2 k)

Reference 5

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no resolver link, observed 2026-08-01T18:37:44.849362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:37:44.849362Z digest=sha256:07ecbb18f7e65e692bc32e04df4122c02ceba61f7bae7534814fc8eb265973ff

Observation c0e2e49d-f37e-4fab-9da1-373a80d3d89c · outbound

This paper cites When all shocks are non- Gaussian the model is a correctly specified, the information identity givesI ψ =J ψ, andV B collapses to (2.15).

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions When all shocks are non- Gaussian the model is a correctly specified, the information identity givesI ψ =J ψ, andV B collapses to (2.15)

Reference 6

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Source-reported events for the cited work

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Observation 135ebff6-bb46-49ae-8591-36ab2592f0a4 · outbound

This paper cites (2016)(Theorem 4.1), a standard Taylor expansion of the bootstrap score completes the proof.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions (2016)(Theorem 4.1), a standard Taylor expansion of the bootstrap score completes the proof

Reference 7

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Observation 89571029-8abe-40a2-940f-cfaa774b8902 · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 8

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Observation 8966dd2c-8b64-4603-be3c-147095cd249e · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 11

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Observation 4bd2b80c-5061-4e06-afe4-470a35631ea0 · outbound

This paper cites By the consistency ofˆλT through Lanne et al.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions By the consistency ofˆλT through Lanne et al

Reference 12

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source=pdf_text observed=2026-08-01T18:37:45.262490Z digest=sha256:a4377d1a8c090788336f148519350eb27745b48f0e68ddd078e931d51ee10964

Observation a453b41f-6adf-4309-820c-7062603ac13d · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 13

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Unavailable: canonical work link unavailable.

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Observation 771821b6-17c5-4c65-aca3-34fbc75c0bd2 · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-01T18:37:45.327866Z digest=sha256:f32fadbc38c4a56a9583b25831a649ae283c4740d6e1e789a6c09e561e11f988

Observation 70f83f91-37b3-4d65-b77b-069a6c6f3600 · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 15

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Observation 01739ccf-bee9-473d-8398-2f1681d3ff49 · outbound

This paper cites and Lunsford, K.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions and Lunsford, K

Reference 172

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source=pdf_text observed=2026-08-01T18:37:44.675706Z digest=sha256:332f6fa807d90a4884adf0f4bca72f4c7050a10e17fe32e129c00f43eacf8def

Observation 5f44ebd3-1f82-43c6-94be-f1157eb8976c · outbound

This paper cites an unresolved cited work.

Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural Vector Autoregressions Unresolved cited work

Reference 1974

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no resolver link, observed 2026-08-01T18:37:45.091407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:37:45.091407Z digest=sha256:2944754f65550999a058165eb69fa5246e638bc6929ddfc1eea264a646aca54c

Pith citing papers

No inbound Pith citation observations are available.