{"id":"W3124213313","doi":"10.1017/s1365100519000890","title":"INTEREST RATES, MONEY, AND ECONOMIC ACTIVITY","year":2019,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Divisia monetary aggregates index; Divisia index; Economics; Broad money; Econometrics; Monetary policy; Shock (circulatory); Monetary economics; Vector autoregression; Industrial production; Granger causality; Autoregressive conditional heteroskedasticity; Macroeconomics; Central bank; Statistics; Mathematics; Quantitative easing; Energy (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004578814,0.0003448279,0.0007418458,0.0002654172,0.00009745426,0.0002059749,0.0003511889,0.0001860972,0.001849818],"category_scores_gemma":[0.00001475706,0.0004449095,0.0001606115,0.00004023183,0.0001149731,0.0006953331,0.0002084906,0.0002710343,0.01196689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006927715,"about_ca_system_score_gemma":0.00002542953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002406629,"about_ca_topic_score_gemma":0.0009316598,"domain_scores_codex":[0.9978863,0.00001753947,0.0006930094,0.000812623,0.00001039416,0.0005801605],"domain_scores_gemma":[0.9986196,0.00008092196,0.000441009,0.0006461601,0.000003571967,0.000208758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001356505,0.00008530357,0.448769,0.00009626825,0.0002941045,0.000004807902,0.000270016,0.005359378,0.00005866163,0.5371619,0.001513214,0.00625163],"study_design_scores_gemma":[0.001489385,0.0001464289,0.08152628,0.00001438197,0.00001081382,0.00004861713,0.00007679861,0.863196,0.00007363573,0.02851623,0.02404459,0.0008568475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660128,0.0002974212,0.0003311988,0.001023052,0.001112424,0.0003485768,0.000721559,0.00006976099,0.03008318],"genre_scores_gemma":[0.9935302,0.0002443137,0.0002687944,0.0004321345,0.0001441057,0.00001711221,0.00005474907,0.00006370067,0.005244934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8578366,"threshold_uncertainty_score":0.9998003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03570398129720395,"score_gpt":0.2291995355919267,"score_spread":0.1934955542947228,"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."}}