{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QVRB6WVZEANH72VC7TVUZTCFZX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0858913e7660386ecd80916fbd3316d0ceaea086bb89c10bd8abe12a65b808a7","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-02-02T17:14:54Z","title_canon_sha256":"15f68698c0af7c8ff1ee29c0d1b9b33e42ab6adf4cf070ba4026318d8edbe6b2"},"schema_version":"1.0","source":{"id":"2302.01233","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.01233","created_at":"2026-06-09T01:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2302.01233v3","created_at":"2026-06-09T01:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.01233","created_at":"2026-06-09T01:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"QVRB6WVZEANH","created_at":"2026-06-09T01:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"QVRB6WVZEANH72VC","created_at":"2026-06-09T01:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"QVRB6WVZ","created_at":"2026-06-09T01:05:02Z"}],"graph_snapshots":[{"event_id":"sha256:cf7238577cf7700ee947518efda199f0125a40012525488a198010d1a4e3b7eb","target":"graph","created_at":"2026-06-09T01:05:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2302.01233/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.","authors_text":"Ines Wilms, Robert Adamek, Stephan Smeekes","cross_cats":["math.ST","stat.ME","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-02-02T17:14:54Z","title":"Sparse High-Dimensional Vector Autoregressive Bootstrap"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.01233","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:06db326182c4e3fe70c1234d9b3761b35324d4fcf3d5749aa03d1f6ca4aa0f26","target":"record","created_at":"2026-06-09T01:05:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0858913e7660386ecd80916fbd3316d0ceaea086bb89c10bd8abe12a65b808a7","cross_cats_sorted":["math.ST","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-02-02T17:14:54Z","title_canon_sha256":"15f68698c0af7c8ff1ee29c0d1b9b33e42ab6adf4cf070ba4026318d8edbe6b2"},"schema_version":"1.0","source":{"id":"2302.01233","kind":"arxiv","version":3}},"canonical_sha256":"85621f5ab9201a7feaa2fceb4ccc45cde83459c23a0d55946811996265889967","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85621f5ab9201a7feaa2fceb4ccc45cde83459c23a0d55946811996265889967","first_computed_at":"2026-06-09T01:05:02.499818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:02.499818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JTniPRhwSUzPpTLpQHGxQzcmjXwrC5Pl70outRAIeJsLpQXVh3sa+1x9P75GJvcADIXXP+cz1afem8JRz2B3CQ==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:02.500290Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.01233","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:06db326182c4e3fe70c1234d9b3761b35324d4fcf3d5749aa03d1f6ca4aa0f26","sha256:cf7238577cf7700ee947518efda199f0125a40012525488a198010d1a4e3b7eb"],"state_sha256":"bcc5ec9fa8edf713f3b582ab9722485c92690fea11c6ec3510c6562046aadd06"}