{"id":"W2114908519","doi":"10.1016/j.jeconom.2011.02.020","title":"Understanding models’ forecasting performance","year":2011,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Econometrics; Uncorrelated; Measure (data warehouse); Computer science; Probabilistic forecasting; Exchange rate; Predictive modelling; Machine learning; Artificial intelligence; Economics; Data mining; Mathematics; Statistics; Macroeconomics","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":[0.009325533,0.0006620876,0.0005159112,0.002069033,0.0004776134,0.00520388,0.0007007261,0.002293488,0.00374671],"category_scores_gemma":[0.06639483,0.0005793008,0.0004805608,0.001245575,0.0006883827,0.007472174,0.0007273902,0.001683004,0.0008972756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146424,"about_ca_system_score_gemma":0.0009043894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007958884,"about_ca_topic_score_gemma":0.004908132,"domain_scores_codex":[0.9980099,0.0009958672,0.0001276835,0.0002785465,0.0004095025,0.0001786432],"domain_scores_gemma":[0.9720474,0.02205904,0.001207207,0.002190926,0.002114194,0.0003811959],"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":[0.0005211357,0.0003352809,0.1191092,0.0001521207,0.0005470568,0.00025071,0.001001622,0.5386431,0.003529739,0.102505,0.02135847,0.2120464],"study_design_scores_gemma":[0.00002263512,0.00005132474,0.01175849,0.00003628794,0.00005162438,0.00003839217,0.0002420183,0.9173706,0.001226506,0.06608513,0.003091144,0.00002593659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8225624,0.004974388,0.107286,0.02300144,0.0006726875,0.00005738737,0.001100084,0.0009401896,0.03940536],"genre_scores_gemma":[0.9910609,0.0005717521,0.006648276,0.0001971531,0.0001511471,0.00001019819,0.000384409,0.00004965292,0.0009264306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009325533,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8732744742821895,"score_gpt":0.2384277875833312,"score_spread":0.6348466866988582,"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."}}