{"id":"W3144740148","doi":"10.2139/ssrn.3577387","title":"Recent Monetary Policy and the Credit Card-Augmented Divisia Monetary Aggregates","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Italy: Economic History and Contemporary Issues","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Divisia monetary aggregates index; Divisia index; Monetary policy; Monetary economics; Economics; Credit card; Financial system; Credit channel; Inflation targeting; Finance; Payment","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":[0.001768235,0.0004241919,0.0005861751,0.0009532601,0.0006045235,0.005391567,0.0007665462,0.001782226,0.003700457],"category_scores_gemma":[0.01013108,0.0003158627,0.0003003539,0.002136924,0.001488217,0.002442177,0.001112145,0.003132503,0.0005684897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498638,"about_ca_system_score_gemma":0.0007212912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005029247,"about_ca_topic_score_gemma":0.003206608,"domain_scores_codex":[0.9995608,0.0001480986,0.00003004289,0.0001396122,0.00007698736,0.00004446136],"domain_scores_gemma":[0.9979572,0.0008095691,0.0005802456,0.0002373739,0.000302468,0.0001131582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001392322,0.0001308925,0.01844753,0.0003715261,0.0001228596,0.0005256569,0.0008662129,0.03325454,0.001489377,0.8067467,0.02040445,0.116248],"study_design_scores_gemma":[0.0003065422,0.0002438773,0.1529205,0.0006441497,0.0002497357,0.0004485802,0.0005375706,0.09949747,0.001604983,0.5474923,0.195903,0.0001512772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7362953,0.04025086,0.02037715,0.05314864,0.003504462,0.00004542893,0.00191083,0.0003212797,0.1441461],"genre_scores_gemma":[0.9782338,0.006371178,0.002000961,0.0004965308,0.002383532,0.00001212682,0.0003262981,0.00004230763,0.01013333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005391567,"threshold_uncertainty_score":0.01812899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641590905762406,"score_gpt":0.1941928024654818,"score_spread":0.1777768934078577,"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."}}