{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001448787,0.0001656549,0.0005482779,0.001666403,0.000109916,0.00005977519,0.0003691083,0.0001026263,0.0009106928],"category_scores_gemma":[0.0001666325,0.0001826461,0.0002346797,0.0005072855,0.00004959934,0.001598187,0.00004738065,0.0002706838,0.0001610189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004045625,"about_ca_system_score_gemma":0.00002409121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004445666,"about_ca_topic_score_gemma":0.000002321851,"domain_scores_codex":[0.9980293,0.000009106758,0.001348335,0.0001961557,0.00003263966,0.0003844697],"domain_scores_gemma":[0.9979798,0.00008681917,0.001494805,0.0002165459,0.00002409817,0.0001979569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002332207,0.0003792586,0.5569245,0.0001565461,0.000670649,0.00005452361,0.006390719,0.04252549,0.000002152181,0.3844891,0.004447947,0.003725901],"study_design_scores_gemma":[0.002043172,0.001085129,0.03001686,0.00006729868,0.00004101917,0.0004946623,0.0007624948,0.4740959,0.00009930376,0.485083,0.005364865,0.0008463788],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7549201,0.001257735,0.05084059,0.00007300507,0.001037824,0.00007702891,0.00002947956,0.00001278359,0.1917515],"genre_scores_gemma":[0.993947,0.0006006556,0.004794142,0.0001602125,0.000238557,8.300161e-7,0.000001161168,0.00002663464,0.0002308599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5269076,"threshold_uncertainty_score":0.9971448,"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."}}