{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:346SLX2CCS6ZQ5ODKK46DSATX7","short_pith_number":"pith:346SLX2C","schema_version":"1.0","canonical_sha256":"df3d25df4214bd9875c352b9e1c813bfffa91138bcf906e420bfccb1078b68ed","source":{"kind":"arxiv","id":"1901.05397","version":1},"attestation_state":"computed","paper":{"title":"lassopack: Model selection and prediction with regularized regression in Stata","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Achim Ahrens, Christian B. Hansen, Mark E. Schaffer","submitted_at":"2019-01-16T17:30:27Z","abstract_excerpt":"This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, "},"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":"1901.05397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2019-01-16T17:30:27Z","cross_cats_sorted":[],"title_canon_sha256":"7b96d29933f305d1fc2fd069e32c073911dc45dceb7a21206514d732464b6e87","abstract_canon_sha256":"77e5a66900c0e5e04bab293907ffa46f5cb62e7c76d9a254d8b357af33169c75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:10.978615Z","signature_b64":"r3Tn9rkbz9QUmIIPjhIkEdXNqEwBJR+e+Q4QYIU0sTPZbQCrtrjAaZdwWWUCFHJP//r9Uku78QbGQdHNyW6JAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df3d25df4214bd9875c352b9e1c813bfffa91138bcf906e420bfccb1078b68ed","last_reissued_at":"2026-05-17T23:56:10.977983Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:10.977983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"lassopack: Model selection and prediction with regularized regression in Stata","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Achim Ahrens, Christian B. Hansen, Mark E. Schaffer","submitted_at":"2019-01-16T17:30:27Z","abstract_excerpt":"This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05397","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":""},"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":"1901.05397","created_at":"2026-05-17T23:56:10.978070+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.05397v1","created_at":"2026-05-17T23:56:10.978070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05397","created_at":"2026-05-17T23:56:10.978070+00:00"},{"alias_kind":"pith_short_12","alias_value":"346SLX2CCS6Z","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"346SLX2CCS6ZQ5OD","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"346SLX2C","created_at":"2026-05-18T12:33:07.085635+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/346SLX2CCS6ZQ5ODKK46DSATX7","json":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7.json","graph_json":"https://pith.science/api/pith-number/346SLX2CCS6ZQ5ODKK46DSATX7/graph.json","events_json":"https://pith.science/api/pith-number/346SLX2CCS6ZQ5ODKK46DSATX7/events.json","paper":"https://pith.science/paper/346SLX2C"},"agent_actions":{"view_html":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7","download_json":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7.json","view_paper":"https://pith.science/paper/346SLX2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.05397&json=true","fetch_graph":"https://pith.science/api/pith-number/346SLX2CCS6ZQ5ODKK46DSATX7/graph.json","fetch_events":"https://pith.science/api/pith-number/346SLX2CCS6ZQ5ODKK46DSATX7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7/action/storage_attestation","attest_author":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7/action/author_attestation","sign_citation":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7/action/citation_signature","submit_replication":"https://pith.science/pith/346SLX2CCS6ZQ5ODKK46DSATX7/action/replication_record"}},"created_at":"2026-05-17T23:56:10.978070+00:00","updated_at":"2026-05-17T23:56:10.978070+00:00"}