{"id":"W4402991023","doi":"10.3390/jrfm17100435","title":"Constructing Divisia Monetary Aggregates for the Asian Tigers","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Global Financial Crisis and Policies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Tenaga Nasional; Tenaga Nasional Berhad","keywords":"Divisia index; Divisia monetary aggregates index; Economics; Monetary economics; Monetary policy; Mathematics; Central bank; Statistics; Quantitative easing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00139023,0.0008216751,0.0004870681,0.005619602,0.0006922171,0.001981742,0.0004742441,0.0004056801,0.001562373],"category_scores_gemma":[0.006585415,0.0002588918,0.0005197568,0.004412417,0.0004506112,0.002010363,0.001616896,0.0008481519,0.0005367812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174905,"about_ca_system_score_gemma":0.0007716123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01351946,"about_ca_topic_score_gemma":0.01063929,"domain_scores_codex":[0.9996086,0.0001091398,0.00005329201,0.00008365369,0.00008691907,0.00005855617],"domain_scores_gemma":[0.9975552,0.0004166294,0.001006135,0.0002383565,0.0005918171,0.0001917794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004502084,0.0001009207,0.8371525,0.0001399165,0.0003339386,0.0006920775,0.003543661,0.03805932,0.001594505,0.01504476,0.005480595,0.09740762],"study_design_scores_gemma":[0.0000371159,0.0002602327,0.8003873,0.0002043079,0.0001779256,0.0003002836,0.006506125,0.1415664,0.001938142,0.02407045,0.02441871,0.0001328723],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981781,0.000313249,0.00845788,0.0001882035,0.00005975235,0.00005525425,0.001882724,0.000116867,0.007145033],"genre_scores_gemma":[0.9918854,0.0001693158,0.00417275,0.00001749827,0.00003399904,0.00005687602,0.002700347,0.00002261742,0.0009412176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01351946,"threshold_uncertainty_score":0.02688158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178909340127373,"score_gpt":0.2122373251579575,"score_spread":0.2004482317566838,"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."}}