{"id":"W1848581994","doi":"10.1111/jmcb.12179","title":"Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?","year":2015,"lang":"en","type":"article","venue":"Journal of money credit and banking","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Survey of Professional Forecasters; Quarter (Canadian coin); Consensus forecast; Variable (mathematics); Econometrics; Economics; Monetary policy; Macroeconomics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001127074,0.0001906435,0.0005633417,0.0005594142,0.0001186877,0.0001664932,0.0002243611,0.00009939053,0.0003398064],"category_scores_gemma":[0.0001144902,0.0001842254,0.0001502819,0.0001078662,0.00003431131,0.0007018756,0.00009146376,0.0002573983,0.00006907064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001353629,"about_ca_system_score_gemma":0.00002100742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005506047,"about_ca_topic_score_gemma":0.00001444711,"domain_scores_codex":[0.9983824,0.00001725461,0.0009058474,0.0002431668,0.00005349487,0.0003978584],"domain_scores_gemma":[0.99814,0.00005671094,0.001270374,0.0001536261,0.00002922725,0.0003500285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004441438,0.000101058,0.8830519,0.00005409316,0.0002981721,0.00009105854,0.004555769,0.004528191,0.00002399865,0.001216478,0.08314559,0.02248957],"study_design_scores_gemma":[0.009333767,0.001693393,0.5506055,0.0009913844,0.000124514,0.001724511,0.003576237,0.05315265,0.0003168044,0.1125069,0.2638289,0.00214537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990138,0.0009031133,0.0003471993,0.002312938,0.001681188,0.0000712668,0.00005760853,0.000007482887,0.004481225],"genre_scores_gemma":[0.9948238,0.00002142921,0.001826222,0.000894513,0.001603985,0.000003240087,0.00000320993,0.00002608418,0.0007975041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3324463,"threshold_uncertainty_score":0.7512495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245070887510419,"score_gpt":0.2579141926995278,"score_spread":0.1334071039484859,"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."}}