{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PHO5JNPKDAVMOLYPRWVQ3YVUFE","short_pith_number":"pith:PHO5JNPK","schema_version":"1.0","canonical_sha256":"79ddd4b5ea182ac72f0f8dab0de2b4291e7ae1061e812bf6c7e62f429fcd2377","source":{"kind":"arxiv","id":"2607.19625","version":1},"attestation_state":"computed","paper":{"title":"Estimating Network Spillovers under Dense Measurement Error","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Aureo de Paula, Weining Wang, Yingxing Li","submitted_at":"2026-07-21T23:16:33Z","abstract_excerpt":"This paper analyzes spillover effects in spatial (network) models when the neighborhood (adjacency) matrix is contaminated by measurement error from reporting, aggregation, or disclosure imperfections, leading to inconsistent estimation of network effects. We introduce a regularization framework for the latent network that allows for sparse and/or low-rank structure and accommodates potential correlation between measurement errors and outcomes. We propose two estimators: (i) a two-stage procedure that first denoises the adjacency matrix and then incorporates the purified network into a regress"},"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":"2607.19625","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2026-07-21T23:16:33Z","cross_cats_sorted":[],"title_canon_sha256":"b7d9b73e959138f960e04c238995a1ae7d2f8137720d373b4d088dfdf2316980","abstract_canon_sha256":"d3fb2475b8cc666ae32dca99e1b5ff3e237e388a2e7529f66fe2f0e4290a769a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T00:24:00.519699Z","signature_b64":"LTaUXOxojYyBRX3kNHfvjaI9NfiX5B+1YmjKXNO+wooWXEzqCNUtRnkTPIp4A8rdWNUwrHHRJLaD8fzumN6ECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79ddd4b5ea182ac72f0f8dab0de2b4291e7ae1061e812bf6c7e62f429fcd2377","last_reissued_at":"2026-07-23T00:24:00.518867Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T00:24:00.518867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Network Spillovers under Dense Measurement Error","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Aureo de Paula, Weining Wang, Yingxing Li","submitted_at":"2026-07-21T23:16:33Z","abstract_excerpt":"This paper analyzes spillover effects in spatial (network) models when the neighborhood (adjacency) matrix is contaminated by measurement error from reporting, aggregation, or disclosure imperfections, leading to inconsistent estimation of network effects. We introduce a regularization framework for the latent network that allows for sparse and/or low-rank structure and accommodates potential correlation between measurement errors and outcomes. We propose two estimators: (i) a two-stage procedure that first denoises the adjacency matrix and then incorporates the purified network into a regress"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19625","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/2607.19625/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":"2607.19625","created_at":"2026-07-23T00:24:00.519292+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.19625v1","created_at":"2026-07-23T00:24:00.519292+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19625","created_at":"2026-07-23T00:24:00.519292+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHO5JNPKDAVM","created_at":"2026-07-23T00:24:00.519292+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHO5JNPKDAVMOLYP","created_at":"2026-07-23T00:24:00.519292+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHO5JNPK","created_at":"2026-07-23T00:24:00.519292+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/PHO5JNPKDAVMOLYPRWVQ3YVUFE","json":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE.json","graph_json":"https://pith.science/api/pith-number/PHO5JNPKDAVMOLYPRWVQ3YVUFE/graph.json","events_json":"https://pith.science/api/pith-number/PHO5JNPKDAVMOLYPRWVQ3YVUFE/events.json","paper":"https://pith.science/paper/PHO5JNPK"},"agent_actions":{"view_html":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE","download_json":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE.json","view_paper":"https://pith.science/paper/PHO5JNPK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.19625&json=true","fetch_graph":"https://pith.science/api/pith-number/PHO5JNPKDAVMOLYPRWVQ3YVUFE/graph.json","fetch_events":"https://pith.science/api/pith-number/PHO5JNPKDAVMOLYPRWVQ3YVUFE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE/action/storage_attestation","attest_author":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE/action/author_attestation","sign_citation":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE/action/citation_signature","submit_replication":"https://pith.science/pith/PHO5JNPKDAVMOLYPRWVQ3YVUFE/action/replication_record"}},"created_at":"2026-07-23T00:24:00.519292+00:00","updated_at":"2026-07-23T00:24:00.519292+00:00"}