{"id":"W2077421290","doi":"10.1002/for.929","title":"Unemployment variation over the business cycles: a comparison of forecasting models","year":2004,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Unemployment; Business cycle; Econometrics; Linear model; Linear regression; Computer science; Series (stratigraphy); Aggregate (composite); Regression; Economics; Mathematics; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002352609,0.0005919772,0.0006060206,0.0009115444,0.0002666705,0.0008243533,0.0008035757,0.0009493026,0.001453616],"category_scores_gemma":[0.006533075,0.0003098628,0.0008744523,0.0008619977,0.0002799007,0.0007556295,0.0003521249,0.000509148,0.0001669131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199599,"about_ca_system_score_gemma":0.0006816611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03824826,"about_ca_topic_score_gemma":0.01618057,"domain_scores_codex":[0.9995636,0.0002314802,0.00002464711,0.00007777277,0.00006370418,0.00003868684],"domain_scores_gemma":[0.9966306,0.002765453,0.0001912407,0.00007996673,0.0002641607,0.00006853145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001399559,0.00005491015,0.006429794,0.00003781915,0.00007838895,0.0000329638,0.00004398866,0.974538,0.0001282603,0.001238875,0.0002403261,0.01703675],"study_design_scores_gemma":[0.000004457858,0.00001483744,0.001389008,0.000006147991,0.00001054806,0.000002998882,0.000007033403,0.9980457,0.00003947822,0.0004239405,0.00005199614,0.000003795999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140709,0.000999892,0.07699357,0.0006472744,0.00009408478,0.00007648148,0.0003705245,0.0002655815,0.006481729],"genre_scores_gemma":[0.992644,0.000322281,0.005676004,0.00002496064,0.00002514089,0.00003289979,0.0002059956,0.00001530328,0.001053561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03824826,"threshold_uncertainty_score":0.07605129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2191910576277073,"score_gpt":0.2730355441331966,"score_spread":0.05384448650548929,"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."}}