{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DHDESHJJFI2SKSWMYDBBZQXTBZ","short_pith_number":"pith:DHDESHJJ","schema_version":"1.0","canonical_sha256":"19c6491d292a35254accc0c21cc2f30e43988c75b151961d8f47b20ad62420bf","source":{"kind":"arxiv","id":"2408.12863","version":2},"attestation_state":"computed","paper":{"title":"Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"econ.EM","authors_text":"Francis X. Diebold, Jingyu He, Junye Li, Siyu Bie","submitted_at":"2024-08-23T06:25:04Z","abstract_excerpt":"We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the tree-growing algorithm to partition macroeconomic variables based on the DNS model's marginal likelihood, thereby identifying regime-shifting patterns in the yield curve. Compared to traditional Markov-switching models, our model offers clear economic interpretation via macroeconomic linkages and ensures computational simplicity. In an empirical application to U.S. Treasury yields, we find (1) important yield-curve regime switching, and (2) e"},"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":"2408.12863","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2024-08-23T06:25:04Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"bf498490109b9f3cfcfdb7832434f8fb86f9f7c7df44097d33827e8079576d96","abstract_canon_sha256":"3ed856de9650c21c6431d44f90fb280510b0d008550adb62c1ebabe77596d019"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:01.844592Z","signature_b64":"7Phx6Rq+gP6hO2OCwF67wvjU+KUm4xF+7jcaYhR2sgr2c7W6wc7qLPyGTSSwgkbtwu7mj/IB5n2IRrw+nac1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19c6491d292a35254accc0c21cc2f30e43988c75b151961d8f47b20ad62420bf","last_reissued_at":"2026-07-05T10:59:01.844014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:01.844014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"econ.EM","authors_text":"Francis X. Diebold, Jingyu He, Junye Li, Siyu Bie","submitted_at":"2024-08-23T06:25:04Z","abstract_excerpt":"We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the tree-growing algorithm to partition macroeconomic variables based on the DNS model's marginal likelihood, thereby identifying regime-shifting patterns in the yield curve. Compared to traditional Markov-switching models, our model offers clear economic interpretation via macroeconomic linkages and ensures computational simplicity. In an empirical application to U.S. Treasury yields, we find (1) important yield-curve regime switching, and (2) e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.12863","kind":"arxiv","version":2},"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/2408.12863/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":"2408.12863","created_at":"2026-07-05T10:59:01.844074+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.12863v2","created_at":"2026-07-05T10:59:01.844074+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.12863","created_at":"2026-07-05T10:59:01.844074+00:00"},{"alias_kind":"pith_short_12","alias_value":"DHDESHJJFI2S","created_at":"2026-07-05T10:59:01.844074+00:00"},{"alias_kind":"pith_short_16","alias_value":"DHDESHJJFI2SKSWM","created_at":"2026-07-05T10:59:01.844074+00:00"},{"alias_kind":"pith_short_8","alias_value":"DHDESHJJ","created_at":"2026-07-05T10:59:01.844074+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ","json":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ.json","graph_json":"https://pith.science/api/pith-number/DHDESHJJFI2SKSWMYDBBZQXTBZ/graph.json","events_json":"https://pith.science/api/pith-number/DHDESHJJFI2SKSWMYDBBZQXTBZ/events.json","paper":"https://pith.science/paper/DHDESHJJ"},"agent_actions":{"view_html":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ","download_json":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ.json","view_paper":"https://pith.science/paper/DHDESHJJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.12863&json=true","fetch_graph":"https://pith.science/api/pith-number/DHDESHJJFI2SKSWMYDBBZQXTBZ/graph.json","fetch_events":"https://pith.science/api/pith-number/DHDESHJJFI2SKSWMYDBBZQXTBZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ/action/storage_attestation","attest_author":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ/action/author_attestation","sign_citation":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ/action/citation_signature","submit_replication":"https://pith.science/pith/DHDESHJJFI2SKSWMYDBBZQXTBZ/action/replication_record"}},"created_at":"2026-07-05T10:59:01.844074+00:00","updated_at":"2026-07-05T10:59:01.844074+00:00"}