{"id":"W3124544947","doi":"","title":"Forecasting with Medium and Large Bayesian VARs","year":2010,"lang":"en","type":"preprint","venue":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian vector autoregression; Bayesian probability; Prior probability; Econometrics; Computer science; Range (aeronautics); Set (abstract data type); Factor analysis; Economics; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005793157,0.0009862995,0.001585694,0.001323083,0.0005127501,0.002957075,0.001560436,0.002422243,0.008107627],"category_scores_gemma":[0.02437513,0.0009866398,0.001112399,0.00158578,0.00130112,0.005058321,0.001469528,0.003144876,0.001616754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095279,"about_ca_system_score_gemma":0.0008256615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007604375,"about_ca_topic_score_gemma":0.007778549,"domain_scores_codex":[0.9987308,0.0005264735,0.00007282229,0.0003462572,0.0002147047,0.0001089044],"domain_scores_gemma":[0.9907012,0.00733537,0.000512332,0.0007206984,0.0004525453,0.000277774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003589348,0.00009178434,0.00337687,0.0001382008,0.0001791508,0.0002011309,0.00008295535,0.7979392,0.0007873536,0.1019002,0.009750385,0.08519378],"study_design_scores_gemma":[0.00001128545,0.00001382289,0.0004912055,0.00001635598,0.00001157484,0.00001779676,0.000008570021,0.959285,0.00009576952,0.03900762,0.001026106,0.0000149107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06138245,0.00306819,0.919277,0.003039121,0.0007529652,0.0000606539,0.001135705,0.001103435,0.01018052],"genre_scores_gemma":[0.8718404,0.002530806,0.09705692,0.0004306714,0.001240996,0.0001350034,0.00241201,0.0003033509,0.02404983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008107627,"threshold_uncertainty_score":0.0306375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03632953966749823,"score_gpt":0.1755397299964101,"score_spread":0.1392101903289119,"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."}}