{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007168212,0.00009439815,0.000355446,0.0001614035,0.00004371778,0.00006630622,0.0001353989,0.00007306912,0.0006420509],"category_scores_gemma":[0.00005065099,0.00009070907,0.0001145121,0.00008103358,0.00001840738,0.0002705613,0.00004533615,0.0002236856,0.00001668813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000290856,"about_ca_system_score_gemma":0.00001301288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006185954,"about_ca_topic_score_gemma":0.00000760142,"domain_scores_codex":[0.9990522,0.00001067753,0.0005902149,0.0001429989,0.00005021946,0.0001536554],"domain_scores_gemma":[0.9991874,0.00005780308,0.0005047785,0.0001404247,0.00003512361,0.00007452099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004737022,0.00002976103,0.9911351,0.00002684973,0.00005194109,0.000008603134,0.0001441292,0.00002860421,0.00005295539,0.003542382,0.001313337,0.003618942],"study_design_scores_gemma":[0.001857028,0.0003851256,0.6907861,0.0001109235,0.00002816574,0.0001059446,0.0001626999,0.1024112,0.00002303301,0.1301922,0.07349746,0.0004401391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765599,0.002882533,0.001211335,0.0004216471,0.0009436853,0.00005842395,0.00001281892,0.000004808946,0.01790481],"genre_scores_gemma":[0.9976966,0.0004963678,0.0008466614,0.0001363398,0.0003793568,3.396754e-7,0.00000176294,0.000009994858,0.0004326137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3003491,"threshold_uncertainty_score":0.7030007,"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."}}