{"id":"W584048904","doi":"10.22004/ag.econ.274619","title":"Least Squares Model Averaging by Prediction Criterion","year":2012,"lang":"en","type":"preprint","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Estimator; Mathematics; Applied mathematics; Mean squared error; Model selection; Inference; Least-squares function approximation; Statistics; Statistical inference; Asymptotic distribution; Mathematical optimization; Computer science; Artificial intelligence","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":[0.004622992,0.001023668,0.001897896,0.00130955,0.000534564,0.001065402,0.002130987,0.0010264,0.00195296],"category_scores_gemma":[0.01891471,0.0005017624,0.001170491,0.001332583,0.0007809328,0.002098628,0.001308575,0.001440792,0.0006260391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007109193,"about_ca_system_score_gemma":0.001475139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003031891,"about_ca_topic_score_gemma":0.002198121,"domain_scores_codex":[0.9963796,0.001847918,0.0001396938,0.0005778517,0.0008928459,0.0001622174],"domain_scores_gemma":[0.9953172,0.002798896,0.0003964419,0.0006935513,0.0007023078,0.00009167366],"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":[0.00008213527,0.00009984385,0.003712964,0.0001919313,0.0003866891,0.0001316301,0.00009554873,0.6390422,0.004710278,0.1294231,0.003983125,0.2181405],"study_design_scores_gemma":[0.00000686111,0.00003691754,0.000358646,0.000007737996,0.00002309881,0.00003928139,0.000006539304,0.9667292,0.0009214068,0.03067204,0.001184509,0.00001376514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002951044,0.00009554392,0.9963036,0.00006676462,0.00001988635,0.00001268223,0.0000222101,0.0001681414,0.0003601358],"genre_scores_gemma":[0.3500366,0.0004468033,0.6450763,0.0002766569,0.0002397812,0.0002620084,0.0006863157,0.0003663769,0.002609169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004622992,"threshold_uncertainty_score":0.02444905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1086888255678956,"score_gpt":0.229200552345724,"score_spread":0.1205117267778284,"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."}}