{"id":"W2153086036","doi":"10.1111/jmcb.12686","title":"Monetary News Shocks","year":2019,"lang":"en","type":"article","venue":"Journal of money credit and banking","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Vector autoregression; Economics; Shock (circulatory); Monetary policy; Monetary economics; Inflation (cosmology); Econometrics; Residual; Forecast error; Autoregressive model; Variance (accounting); Interest rate; Mathematics","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":[0.0003829295,0.0002402687,0.000229879,0.0008306978,0.0002705818,0.00163942,0.0001713773,0.0007366622,0.004435957],"category_scores_gemma":[0.005192087,0.0001333478,0.0001556147,0.0007946062,0.0002250317,0.0008100901,0.0004342062,0.0009197118,0.0005317103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005640449,"about_ca_system_score_gemma":0.0002106088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002674922,"about_ca_topic_score_gemma":0.001852401,"domain_scores_codex":[0.9998203,0.00003224239,0.00001727125,0.00003465212,0.00006928134,0.00002631419],"domain_scores_gemma":[0.9984165,0.0005665894,0.0005656867,0.00006027354,0.0003045612,0.00008635251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001519765,0.0005720874,0.5331479,0.0006673369,0.0003644394,0.001900408,0.001444258,0.05379568,0.009249433,0.1603163,0.04754998,0.1894725],"study_design_scores_gemma":[0.0001834229,0.0006776841,0.7079001,0.0006818851,0.0002896718,0.0007151827,0.002746048,0.1120032,0.01228508,0.06842856,0.09393713,0.0001520187],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401079,0.001697096,0.005834204,0.005539467,0.0005869933,0.00006093188,0.004447831,0.0001587895,0.04156675],"genre_scores_gemma":[0.995068,0.000662405,0.0003664644,0.0002461311,0.0002073814,0.00001125247,0.0008717942,0.00000982565,0.002556748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004435957,"threshold_uncertainty_score":0.01483977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675133400274904,"score_gpt":0.2068330539807395,"score_spread":0.1900817199779905,"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."}}