{"id":"W2772952059","doi":"10.1007/s40953-017-0114-3","title":"Forecasting Inflation Rate: Professional Against Academic, Which One is More Accurate","year":2017,"lang":"en","type":"article","venue":"Journal of Quantitative Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Survey of Professional Forecasters; Inflation (cosmology); Economics; Term (time); Quarter (Canadian coin); Consensus forecast; Finance; Causality (physics); Aggregate (composite); Econometrics; Monetary economics; Monetary policy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00225412,0.000249107,0.0007991792,0.0003533786,0.0005254276,0.0002510152,0.0006762908,0.0002296958,0.0002743494],"category_scores_gemma":[0.00128518,0.0002767502,0.00023163,0.00005688154,0.00013182,0.002834752,0.0001260941,0.0006773099,0.000275915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002268406,"about_ca_system_score_gemma":0.0001075883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005005086,"about_ca_topic_score_gemma":0.00002244385,"domain_scores_codex":[0.9973894,0.00003687552,0.00182604,0.0003152805,0.00003347932,0.0003989501],"domain_scores_gemma":[0.9935797,0.000238764,0.005461479,0.0004050474,0.0001190177,0.0001960019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002213641,0.0005982114,0.5299481,0.0003563144,0.002598045,0.0000426271,0.02346501,0.1217741,0.00071416,0.2791939,0.02889702,0.01019877],"study_design_scores_gemma":[0.003987273,0.0005041361,0.2582048,0.0003376672,0.00005717263,0.0000522133,0.001068135,0.5977615,0.001133242,0.1091204,0.02666617,0.001107266],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841417,0.0005100741,0.0008867605,0.0076471,0.001464045,0.000174171,0.0002424454,0.000007237064,0.004926469],"genre_scores_gemma":[0.9930853,0.001295605,0.003162269,0.001095331,0.0006133009,0.000004316227,0.00001412705,0.00003910145,0.0006905962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4759874,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3044163933972581,"score_gpt":0.352110604583582,"score_spread":0.04769421118632394,"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."}}