{"id":"W2563942437","doi":"10.1002/for.2501","title":"Extracting information shocks from the Bank of England inflation density forecasts","year":2017,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad de Cantabria; University of Leicester","keywords":"Inflation (cosmology); Shock (circulatory); Econometrics; Variable (mathematics); Variance (accounting); Ex-ante; Set (abstract data type); Economics; Measure (data warehouse); Point (geometry); Quarter (Canadian coin); Computer science; Macroeconomics; Mathematics; Data mining; Accounting","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001429338,0.0004269728,0.0005772383,0.003980411,0.000177375,0.001509475,0.0003876457,0.0006643279,0.001229765],"category_scores_gemma":[0.02649025,0.0005313688,0.0002393471,0.002943869,0.0002548798,0.001308142,0.0007858363,0.0009880884,0.000689556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008053319,"about_ca_system_score_gemma":0.0005436261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01503442,"about_ca_topic_score_gemma":0.008774607,"domain_scores_codex":[0.9989862,0.0001729151,0.0001149563,0.0001584667,0.0004819224,0.00008556955],"domain_scores_gemma":[0.9888508,0.006471859,0.001640639,0.0009298038,0.001966008,0.000140912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001481248,0.0001592348,0.3531647,0.000465328,0.0002973714,0.001180053,0.001526082,0.2162781,0.01019578,0.01337443,0.01561908,0.3862585],"study_design_scores_gemma":[0.00006005167,0.0001471646,0.3877113,0.0001275904,0.0001035755,0.0002801295,0.0004428931,0.5770412,0.00859585,0.01380661,0.01150297,0.0001806649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.867097,0.0009844097,0.1080386,0.0009041087,0.0002616883,0.0001302142,0.01492429,0.001344606,0.006315165],"genre_scores_gemma":[0.9823552,0.0003685415,0.0114472,0.00002808215,0.0001000852,0.00002306479,0.004758742,0.00003614564,0.0008829521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01503442,"threshold_uncertainty_score":0.02989388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1178206560646978,"score_gpt":0.2414786714441087,"score_spread":0.1236580153794109,"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."}}