{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OUVISUCPX63GZWQNZRC2TXAIWK","short_pith_number":"pith:OUVISUCP","schema_version":"1.0","canonical_sha256":"752a89504fbfb66cda0dcc45a9dc08b2b53bd66abe3d6d756634485c2efd5e1e","source":{"kind":"arxiv","id":"2506.11551","version":1},"attestation_state":"computed","paper":{"title":"Let the Tree Decide: FABART A Non-Parametric Factor Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Sofia Velasco","submitted_at":"2025-06-13T08:02:35Z","abstract_excerpt":"This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART to linear alternatives and a Monte Carlo experiment demonstrate that the framework accurately recove"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.11551","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"econ.EM","submitted_at":"2025-06-13T08:02:35Z","cross_cats_sorted":[],"title_canon_sha256":"0344ca948cbd6a98fe707b56373fb0e19f2607adca1279d1d9a47d5965c66fae","abstract_canon_sha256":"5109b47184c2a874643f8c29f7e39e839d52629f76b79b01d49854270824d21f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:06.551659Z","signature_b64":"GcO3GT+QI7naE/hTmnbGZ9vL3UHHNIEvDhA+LSGhxxtKp2aJn3GCm5p7nyxa/xMuKlpm+nAm+Y7Z4+LZs5YOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"752a89504fbfb66cda0dcc45a9dc08b2b53bd66abe3d6d756634485c2efd5e1e","last_reissued_at":"2026-07-05T11:21:06.551233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:06.551233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Let the Tree Decide: FABART A Non-Parametric Factor Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Sofia Velasco","submitted_at":"2025-06-13T08:02:35Z","abstract_excerpt":"This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART to linear alternatives and a Monte Carlo experiment demonstrate that the framework accurately recove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11551","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/2506.11551/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.11551","created_at":"2026-07-05T11:21:06.551291+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11551v1","created_at":"2026-07-05T11:21:06.551291+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11551","created_at":"2026-07-05T11:21:06.551291+00:00"},{"alias_kind":"pith_short_12","alias_value":"OUVISUCPX63G","created_at":"2026-07-05T11:21:06.551291+00:00"},{"alias_kind":"pith_short_16","alias_value":"OUVISUCPX63GZWQN","created_at":"2026-07-05T11:21:06.551291+00:00"},{"alias_kind":"pith_short_8","alias_value":"OUVISUCP","created_at":"2026-07-05T11:21:06.551291+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04631","citing_title":"Clustered Local Projections for Short and Ultra-Short Time Series -- A Hierarchical Bayesian Framework","ref_index":92,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK","json":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK.json","graph_json":"https://pith.science/api/pith-number/OUVISUCPX63GZWQNZRC2TXAIWK/graph.json","events_json":"https://pith.science/api/pith-number/OUVISUCPX63GZWQNZRC2TXAIWK/events.json","paper":"https://pith.science/paper/OUVISUCP"},"agent_actions":{"view_html":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK","download_json":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK.json","view_paper":"https://pith.science/paper/OUVISUCP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11551&json=true","fetch_graph":"https://pith.science/api/pith-number/OUVISUCPX63GZWQNZRC2TXAIWK/graph.json","fetch_events":"https://pith.science/api/pith-number/OUVISUCPX63GZWQNZRC2TXAIWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK/action/storage_attestation","attest_author":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK/action/author_attestation","sign_citation":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK/action/citation_signature","submit_replication":"https://pith.science/pith/OUVISUCPX63GZWQNZRC2TXAIWK/action/replication_record"}},"created_at":"2026-07-05T11:21:06.551291+00:00","updated_at":"2026-07-05T11:21:06.551291+00:00"}