{"id":"W6924240729","doi":"10.15456/jae.2022327.072406","title":"How is machine learning useful for macroeconomic forecasting? (replication data)","year":2022,"lang":"en","type":"other","venue":"ZBW Journal Data Archive","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Matching (statistics); Nonlinear system; Macroeconomic model; Stability (learning theory); Economic model","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":[0.01323749,0.0008391641,0.001504254,0.002600851,0.001084348,0.004406503,0.002578431,0.001788106,0.09775633],"category_scores_gemma":[0.1177322,0.0005458406,0.00155745,0.005596043,0.001243446,0.004009307,0.002696833,0.003171115,0.05481133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001290687,"about_ca_system_score_gemma":0.002105972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426094,"about_ca_topic_score_gemma":0.01213849,"domain_scores_codex":[0.9931244,0.002936902,0.000659695,0.001166524,0.001809725,0.000302775],"domain_scores_gemma":[0.8781688,0.03325522,0.006271824,0.06376873,0.01697256,0.00156295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001706831,0.0004238474,0.04018212,0.0009013355,0.0004591052,0.0002952555,0.00058043,0.008602835,0.001351519,0.03416687,0.7148205,0.1965093],"study_design_scores_gemma":[0.0008254811,0.0003169888,0.0661919,0.001255706,0.0002910898,0.0005664106,0.0009957544,0.020647,0.005319046,0.09539666,0.8079342,0.0002596778],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1113809,0.008931009,0.07909229,0.05949808,0.006706467,0.0007722001,0.5691489,0.008156141,0.156314],"genre_scores_gemma":[0.4767292,0.003138884,0.08022258,0.008342727,0.002675384,0.001927028,0.3270861,0.005809409,0.09406869],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09775633,"threshold_uncertainty_score":0.3270275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1273265170170179,"score_gpt":0.3168031368755693,"score_spread":0.1894766198585515,"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."}}