{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:R3ZDQ4MCEFJCYANTB4TUPGWLGM","short_pith_number":"pith:R3ZDQ4MC","canonical_record":{"source":{"id":"2101.04771","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2021-01-12T21:56:12Z","cross_cats_sorted":[],"title_canon_sha256":"b97bb8d82a6a29a61efb571a7a470e41ab93c599b9e7d7234ab30b39a20145f9","abstract_canon_sha256":"763d08d7d029d490088b06867a8a761a28e95505850f1f0f7d10b6a6ee3b15be"},"schema_version":"1.0"},"canonical_sha256":"8ef238718221522c01b30f27479acb33066798d421b9d7911a752ae132cebebe","source":{"kind":"arxiv","id":"2101.04771","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.04771","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"arxiv_version","alias_value":"2101.04771v2","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.04771","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_12","alias_value":"R3ZDQ4MCEFJC","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_16","alias_value":"R3ZDQ4MCEFJCYANT","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_8","alias_value":"R3ZDQ4MC","created_at":"2026-07-05T09:36:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:R3ZDQ4MCEFJCYANTB4TUPGWLGM","target":"record","payload":{"canonical_record":{"source":{"id":"2101.04771","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2021-01-12T21:56:12Z","cross_cats_sorted":[],"title_canon_sha256":"b97bb8d82a6a29a61efb571a7a470e41ab93c599b9e7d7234ab30b39a20145f9","abstract_canon_sha256":"763d08d7d029d490088b06867a8a761a28e95505850f1f0f7d10b6a6ee3b15be"},"schema_version":"1.0"},"canonical_sha256":"8ef238718221522c01b30f27479acb33066798d421b9d7911a752ae132cebebe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:34.522662Z","signature_b64":"/PPnjaBV718zIEZDWhcGOKPLdp5L2eqMoi8Tnzz8lamK7tPJY6ruMcbQQsWxXAya8aAHcTBJo+9Hez5vAPN+Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ef238718221522c01b30f27479acb33066798d421b9d7911a752ae132cebebe","last_reissued_at":"2026-07-05T09:36:34.522247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:34.522247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.04771","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2CS1j1eUhz2VAKEHCKpQSkgEbJ2gZac2jCzcmjIR5R+GE+mYOwK6agt3Qycxc2ak/EfJaSeV8Yv0cY3rKLRZCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:48:16.095135Z"},"content_sha256":"961566c4aef20821ccf54e9858cfc6c902ce1fe339004da17c8e4725cf40f678","schema_version":"1.0","event_id":"sha256:961566c4aef20821ccf54e9858cfc6c902ce1fe339004da17c8e4725cf40f678"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:R3ZDQ4MCEFJCYANTB4TUPGWLGM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Laura Liu, Mikkel Plagborg-M{\\o}ller","submitted_at":"2021-01-12T21:56:12Z","abstract_excerpt":"We develop a generally applicable full-information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross sections of micro data. To handle unobserved aggregate state variables that affect cross-sectional distributions, we compute a numerically unbiased estimate of the model-implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustratio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.04771","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/2101.04771/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":"2026-07-05T09:36:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P+8x1Cp0tiqCua8CCu3QrYjXgvTDa4vKYsa94mhS7SYFThETQddBLEtuyu70q7lHGKpzv8wcYeTLBzRitx5iDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:48:16.095710Z"},"content_sha256":"64189a616e332dcff4dd28b53a1019a9efe2b4d707caba65ee9b38252e969307","schema_version":"1.0","event_id":"sha256:64189a616e332dcff4dd28b53a1019a9efe2b4d707caba65ee9b38252e969307"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/bundle.json","state_url":"https://pith.science/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/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":"2026-08-11T00:48:16Z","links":{"resolver":"https://pith.science/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM","bundle":"https://pith.science/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/bundle.json","state":"https://pith.science/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R3ZDQ4MCEFJCYANTB4TUPGWLGM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:R3ZDQ4MCEFJCYANTB4TUPGWLGM","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":"763d08d7d029d490088b06867a8a761a28e95505850f1f0f7d10b6a6ee3b15be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2021-01-12T21:56:12Z","title_canon_sha256":"b97bb8d82a6a29a61efb571a7a470e41ab93c599b9e7d7234ab30b39a20145f9"},"schema_version":"1.0","source":{"id":"2101.04771","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.04771","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"arxiv_version","alias_value":"2101.04771v2","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.04771","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_12","alias_value":"R3ZDQ4MCEFJC","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_16","alias_value":"R3ZDQ4MCEFJCYANT","created_at":"2026-07-05T09:36:34Z"},{"alias_kind":"pith_short_8","alias_value":"R3ZDQ4MC","created_at":"2026-07-05T09:36:34Z"}],"graph_snapshots":[{"event_id":"sha256:64189a616e332dcff4dd28b53a1019a9efe2b4d707caba65ee9b38252e969307","target":"graph","created_at":"2026-07-05T09:36:34Z","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/2101.04771/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We develop a generally applicable full-information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross sections of micro data. To handle unobserved aggregate state variables that affect cross-sectional distributions, we compute a numerically unbiased estimate of the model-implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustratio","authors_text":"Laura Liu, Mikkel Plagborg-M{\\o}ller","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2021-01-12T21:56:12Z","title":"Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.04771","kind":"arxiv","version":2},"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":"sha256:961566c4aef20821ccf54e9858cfc6c902ce1fe339004da17c8e4725cf40f678","target":"record","created_at":"2026-07-05T09:36:34Z","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":"763d08d7d029d490088b06867a8a761a28e95505850f1f0f7d10b6a6ee3b15be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2021-01-12T21:56:12Z","title_canon_sha256":"b97bb8d82a6a29a61efb571a7a470e41ab93c599b9e7d7234ab30b39a20145f9"},"schema_version":"1.0","source":{"id":"2101.04771","kind":"arxiv","version":2}},"canonical_sha256":"8ef238718221522c01b30f27479acb33066798d421b9d7911a752ae132cebebe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ef238718221522c01b30f27479acb33066798d421b9d7911a752ae132cebebe","first_computed_at":"2026-07-05T09:36:34.522247Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:34.522247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/PPnjaBV718zIEZDWhcGOKPLdp5L2eqMoi8Tnzz8lamK7tPJY6ruMcbQQsWxXAya8aAHcTBJo+9Hez5vAPN+Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:34.522662Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.04771","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:961566c4aef20821ccf54e9858cfc6c902ce1fe339004da17c8e4725cf40f678","sha256:64189a616e332dcff4dd28b53a1019a9efe2b4d707caba65ee9b38252e969307"],"state_sha256":"6a7a89dd14be0efec9dd966ac8fab32672acc64010fbd069207ecd4bd61dd005"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Y+uS0xaHgAn0CLdlJbaFcP8vHTjp4UvOwV/ZjveumaTgQPcwSor8hK+IIvq5NqcNAlbFhUArRC7CDevvuvKCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T00:48:16.100789Z","bundle_sha256":"4a1e141e439d3e1fb1c26d2a1c54828b52fe7314478e28c9a2784fa17f74c557"}}