{"id":"W4394947701","doi":"10.48550/arxiv.2404.11057","title":"Partial Identification of Structural Vector Autoregressions with Non-Centred Stochastic Volatility","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Heteroscedasticity; Inference; Identification (biology); Econometrics; Bayesian probability; Economics; Bayesian inference; Computer science; Artificial intelligence; Biology","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":[0.003392941,0.0008692697,0.001191054,0.001084275,0.0003191467,0.001548598,0.001372418,0.00128605,0.001638819],"category_scores_gemma":[0.02027136,0.0007173243,0.001377065,0.001106623,0.001532697,0.00238975,0.001687226,0.001754366,0.0002908405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007888051,"about_ca_system_score_gemma":0.001202129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003284818,"about_ca_topic_score_gemma":0.002642835,"domain_scores_codex":[0.9985252,0.0005229812,0.00008432611,0.0003756562,0.0003071303,0.0001847691],"domain_scores_gemma":[0.9923158,0.004589983,0.001501584,0.000879857,0.0005429427,0.0001698384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004960301,0.00004311097,0.005221477,0.00009389722,0.0001345519,0.0001573378,0.000206751,0.7484827,0.001832949,0.2119136,0.0003635052,0.03150048],"study_design_scores_gemma":[0.000005763688,0.00003008627,0.001692716,0.00001472968,0.00001455849,0.00002677123,0.00002521875,0.896988,0.0006495246,0.1002,0.0003332509,0.00001942053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06977839,0.0001344989,0.9283831,0.0002087376,0.00001796504,0.00002352182,0.00009847643,0.0001066135,0.001248732],"genre_scores_gemma":[0.9384184,0.0003410618,0.05864469,0.00006832498,0.00005082549,0.00006115029,0.0002658901,0.00005031352,0.002099287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003392941,"threshold_uncertainty_score":0.01794374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02684173454163169,"score_gpt":0.1770591199216757,"score_spread":0.150217385380044,"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."}}