{"id":"W4398589202","doi":"10.7910/dvn/dgykyj","title":"Replication Data for: Endogenous Time Variation in Vector Autoregressions","year":2021,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Replication (statistics); Endogeny; Variation (astronomy); Computer science; Econometrics; Biology; Economics; Virology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003630144,0.00197203,0.00164285,0.002053945,0.0009181809,0.002957132,0.004853726,0.003313703,0.07788901],"category_scores_gemma":[0.02691288,0.0009153586,0.002060434,0.004573814,0.0005429646,0.001414424,0.002707043,0.002452868,0.07838734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185686,"about_ca_system_score_gemma":0.002964945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01550739,"about_ca_topic_score_gemma":0.030298,"domain_scores_codex":[0.9981451,0.0005479676,0.0003347452,0.0003845002,0.0003898694,0.0001977698],"domain_scores_gemma":[0.9907421,0.003225375,0.0007854155,0.003220253,0.001552592,0.0004742318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009499311,0.00002271897,0.0009707972,0.0008618822,0.00005957582,0.00002107021,0.00002243999,0.0002244681,0.00006077043,0.0007615065,0.994213,0.002686815],"study_design_scores_gemma":[0.001844851,0.00003967804,0.00800837,0.0008182029,0.0001217327,0.0001354382,0.0000814146,0.00087346,0.0004314983,0.005569115,0.9820066,0.00006963169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002062421,0.0001768519,0.0002209045,0.0001966598,0.00006536329,0.00002405021,0.9981324,0.0006181992,0.0003592483],"genre_scores_gemma":[0.001351551,0.0001230416,0.001032831,0.000162507,0.00002578796,0.0003389768,0.9962005,0.0002100444,0.0005546633],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07788901,"threshold_uncertainty_score":0.2605647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1764763623918957,"score_gpt":0.3819051789599777,"score_spread":0.205428816568082,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}