{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H3J6ZG27EGMRQXB56BKWSKCSFQ","short_pith_number":"pith:H3J6ZG27","schema_version":"1.0","canonical_sha256":"3ed3ec9b5f2199185c3df0556928522c292c94a42e8bc1ce09fd94ad848369ba","source":{"kind":"arxiv","id":"2606.12739","version":1},"attestation_state":"computed","paper":{"title":"Estimating Semiparametric and Nonparametric Fixed Effects Panel Data Models with mgcv","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Ivan Korolev","submitted_at":"2026-06-10T22:59:03Z","abstract_excerpt":"This paper provides a practical guide to estimating semiparametric and nonparametric fixed-effects panel data models using the mgcv package in R. The focus is implementation: handling fixed effects with unit indicators, first differencing, or penalized unit effects; specifying smooth terms; and conducting cluster-robust inference. Monte Carlo experiments compare \\code{mgcv::bam} estimators with linear and fixed-series spline estimators. Simulations suggest that penalized splines adapt to unknown smoothness and estimate functions accurately in the designs studied here. A penalty-adjusted cluste"},"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":"2606.12739","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2026-06-10T22:59:03Z","cross_cats_sorted":[],"title_canon_sha256":"07ed4c8ae928236bfe7d5a22a50cbc75f1999a1906b8b430fd61e27e67c9393f","abstract_canon_sha256":"a15345c7bd4b1b8d48962427beb73a7bb1f1ec5e5895a8cfe08d0b26419afcde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:48.592131Z","signature_b64":"4llsf9Tr3M5eEBfLHcD27Jgq2ZSG4qwZteADOZ0QDcQ6wIMtBQ0F8kZJA4XbXiYnn3ug2uUXx0ccpDMDyQ0dAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ed3ec9b5f2199185c3df0556928522c292c94a42e8bc1ce09fd94ad848369ba","last_reissued_at":"2026-06-12T01:08:48.591200Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:48.591200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Semiparametric and Nonparametric Fixed Effects Panel Data Models with mgcv","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Ivan Korolev","submitted_at":"2026-06-10T22:59:03Z","abstract_excerpt":"This paper provides a practical guide to estimating semiparametric and nonparametric fixed-effects panel data models using the mgcv package in R. The focus is implementation: handling fixed effects with unit indicators, first differencing, or penalized unit effects; specifying smooth terms; and conducting cluster-robust inference. Monte Carlo experiments compare \\code{mgcv::bam} estimators with linear and fixed-series spline estimators. Simulations suggest that penalized splines adapt to unknown smoothness and estimate functions accurately in the designs studied here. A penalty-adjusted cluste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12739","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/2606.12739/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":"2606.12739","created_at":"2026-06-12T01:08:48.591386+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12739v1","created_at":"2026-06-12T01:08:48.591386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12739","created_at":"2026-06-12T01:08:48.591386+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3J6ZG27EGMR","created_at":"2026-06-12T01:08:48.591386+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3J6ZG27EGMRQXB5","created_at":"2026-06-12T01:08:48.591386+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3J6ZG27","created_at":"2026-06-12T01:08:48.591386+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/H3J6ZG27EGMRQXB56BKWSKCSFQ","json":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ.json","graph_json":"https://pith.science/api/pith-number/H3J6ZG27EGMRQXB56BKWSKCSFQ/graph.json","events_json":"https://pith.science/api/pith-number/H3J6ZG27EGMRQXB56BKWSKCSFQ/events.json","paper":"https://pith.science/paper/H3J6ZG27"},"agent_actions":{"view_html":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ","download_json":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ.json","view_paper":"https://pith.science/paper/H3J6ZG27","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12739&json=true","fetch_graph":"https://pith.science/api/pith-number/H3J6ZG27EGMRQXB56BKWSKCSFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/H3J6ZG27EGMRQXB56BKWSKCSFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ/action/storage_attestation","attest_author":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ/action/author_attestation","sign_citation":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ/action/citation_signature","submit_replication":"https://pith.science/pith/H3J6ZG27EGMRQXB56BKWSKCSFQ/action/replication_record"}},"created_at":"2026-06-12T01:08:48.591386+00:00","updated_at":"2026-06-12T01:08:48.591386+00:00"}