{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:EIQFCLXCZGVGRBDV2ZYJGEPFL6","short_pith_number":"pith:EIQFCLXC","canonical_record":{"source":{"id":"2311.12671","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-11-21T15:29:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a41ae6d92929ae68cdf6f7488912f6c5bc6f24e262648e5e07c73fdf49e1c71c","abstract_canon_sha256":"1b2e23712ff238d2fd4228da9ef6431d934c9779f2d75ab29393ffad80093443"},"schema_version":"1.0"},"canonical_sha256":"2220512ee2c9aa688475d6709311e55f87d053e898b05763c5efc3f473144d42","source":{"kind":"arxiv","id":"2311.12671","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12671","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12671v1","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12671","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_12","alias_value":"EIQFCLXCZGVG","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_16","alias_value":"EIQFCLXCZGVGRBDV","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_8","alias_value":"EIQFCLXC","created_at":"2026-07-05T07:15:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:EIQFCLXCZGVGRBDV2ZYJGEPFL6","target":"record","payload":{"canonical_record":{"source":{"id":"2311.12671","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-11-21T15:29:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a41ae6d92929ae68cdf6f7488912f6c5bc6f24e262648e5e07c73fdf49e1c71c","abstract_canon_sha256":"1b2e23712ff238d2fd4228da9ef6431d934c9779f2d75ab29393ffad80093443"},"schema_version":"1.0"},"canonical_sha256":"2220512ee2c9aa688475d6709311e55f87d053e898b05763c5efc3f473144d42","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:09.534471Z","signature_b64":"F9K9+rwHO0KEypeWpu6dn8cFytSOttAVnVEGCSfemE8qitDo+k9OHja2p236Nv6ok7wgYvGOW0XfGDOiR2zRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2220512ee2c9aa688475d6709311e55f87d053e898b05763c5efc3f473144d42","last_reissued_at":"2026-07-05T07:15:09.534035Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:09.534035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.12671","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:15:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1jliz5qJyMOqDimfVu8xrQfCWI7JUWLVC5PelzTVNMXkiHVyHQvug9te8fno6v5G9ASEd4FRSWMOp8Wntn3rAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:42:56.039544Z"},"content_sha256":"7b40198f330445c397953a7cfc5c674cb171fb6792ef09c944f48df9d184b9af","schema_version":"1.0","event_id":"sha256:7b40198f330445c397953a7cfc5c674cb171fb6792ef09c944f48df9d184b9af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:EIQFCLXCZGVGRBDV2ZYJGEPFL6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predictive Density Combination Using a Tree-Based Synthesis Function","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"econ.EM","authors_text":"Florian Huber, Gary Koop, James Mitchell, Niko Hauzenberger, Tony Chernis","submitted_at":"2023-11-21T15:29:09Z","abstract_excerpt":"Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a ``synthesis'' function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12671","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.12671/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:15:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KJHy7D+2xJHCj9eViKwSPnn0KzbceaChoW5zoeoUjZ1qXmsxsmGbBAWGSJn7rpSR3E2HaU4wZonaoM+KbP0wBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:42:56.040019Z"},"content_sha256":"f284053acef0dd0d400b6302ba49f947ed5845a9d38b86ddb84eae34908b534f","schema_version":"1.0","event_id":"sha256:f284053acef0dd0d400b6302ba49f947ed5845a9d38b86ddb84eae34908b534f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/bundle.json","state_url":"https://pith.science/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T16:42:56Z","links":{"resolver":"https://pith.science/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6","bundle":"https://pith.science/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/bundle.json","state":"https://pith.science/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EIQFCLXCZGVGRBDV2ZYJGEPFL6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:EIQFCLXCZGVGRBDV2ZYJGEPFL6","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":"1b2e23712ff238d2fd4228da9ef6431d934c9779f2d75ab29393ffad80093443","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-11-21T15:29:09Z","title_canon_sha256":"a41ae6d92929ae68cdf6f7488912f6c5bc6f24e262648e5e07c73fdf49e1c71c"},"schema_version":"1.0","source":{"id":"2311.12671","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12671","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12671v1","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12671","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_12","alias_value":"EIQFCLXCZGVG","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_16","alias_value":"EIQFCLXCZGVGRBDV","created_at":"2026-07-05T07:15:09Z"},{"alias_kind":"pith_short_8","alias_value":"EIQFCLXC","created_at":"2026-07-05T07:15:09Z"}],"graph_snapshots":[{"event_id":"sha256:f284053acef0dd0d400b6302ba49f947ed5845a9d38b86ddb84eae34908b534f","target":"graph","created_at":"2026-07-05T07:15:09Z","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/2311.12671/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a ``synthesis'' function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from ","authors_text":"Florian Huber, Gary Koop, James Mitchell, Niko Hauzenberger, Tony Chernis","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-11-21T15:29:09Z","title":"Predictive Density Combination Using a Tree-Based Synthesis Function"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12671","kind":"arxiv","version":1},"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":"sha256:7b40198f330445c397953a7cfc5c674cb171fb6792ef09c944f48df9d184b9af","target":"record","created_at":"2026-07-05T07:15:09Z","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":"1b2e23712ff238d2fd4228da9ef6431d934c9779f2d75ab29393ffad80093443","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2023-11-21T15:29:09Z","title_canon_sha256":"a41ae6d92929ae68cdf6f7488912f6c5bc6f24e262648e5e07c73fdf49e1c71c"},"schema_version":"1.0","source":{"id":"2311.12671","kind":"arxiv","version":1}},"canonical_sha256":"2220512ee2c9aa688475d6709311e55f87d053e898b05763c5efc3f473144d42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2220512ee2c9aa688475d6709311e55f87d053e898b05763c5efc3f473144d42","first_computed_at":"2026-07-05T07:15:09.534035Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:15:09.534035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F9K9+rwHO0KEypeWpu6dn8cFytSOttAVnVEGCSfemE8qitDo+k9OHja2p236Nv6ok7wgYvGOW0XfGDOiR2zRBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:15:09.534471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.12671","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b40198f330445c397953a7cfc5c674cb171fb6792ef09c944f48df9d184b9af","sha256:f284053acef0dd0d400b6302ba49f947ed5845a9d38b86ddb84eae34908b534f"],"state_sha256":"955f599c3a2eed59a2a3e8effb7bc5f532eeb8268c3e5d82222cb56a0814a184"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0qxHAdX7AdIxyHCApHGWX3KyBITdTiELnhNKXpPa3IkA10TAYUKmgZ9KwbV/mFv+DMYbUcEyAvAdgrLziQlxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:42:56.044643Z","bundle_sha256":"8a62129269d5c9ab3cb2beb2dc608fcdb5ba2533a06d759d2e9fa125a757a19f"}}