{"id":"W2799877605","doi":"10.1142/9789813220447_0002","title":"High-mixed-frequency forecasting models for GDP and inflation","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Inflation (cosmology); Data set; Computer science; Dynamic factor; Set (abstract data type); Regression; Regression analysis; Quarter (Canadian coin); Economics; Statistics; Mathematics; Geography; Machine learning; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004296482,0.0007087684,0.0008857858,0.0009880159,0.0003110583,0.001422892,0.001549295,0.001157486,0.003377097],"category_scores_gemma":[0.01197718,0.0005665672,0.00121477,0.001225332,0.0004584143,0.002248654,0.000750082,0.00132823,0.0005576608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006248504,"about_ca_system_score_gemma":0.0004292394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00555306,"about_ca_topic_score_gemma":0.005199947,"domain_scores_codex":[0.9988937,0.0006222874,0.00005475845,0.0002062099,0.0001325924,0.00009054233],"domain_scores_gemma":[0.9948804,0.003781199,0.0005173726,0.0003846787,0.0003466878,0.00008966599],"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":[0.0001590817,0.00009495202,0.009578726,0.00008658103,0.0001743815,0.000113813,0.0002624065,0.7711408,0.0007309181,0.1533791,0.00168464,0.06259471],"study_design_scores_gemma":[0.000006168938,0.00001198723,0.0005635977,0.000006032825,0.000009331908,0.000011582,0.00001339109,0.9806117,0.00007322342,0.01832159,0.0003637208,0.000007686956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09943195,0.0004860414,0.8958008,0.0005650183,0.0001181721,0.00004515319,0.0005222145,0.0003096284,0.002720943],"genre_scores_gemma":[0.8564743,0.0006770383,0.1351408,0.0001289432,0.0001885089,0.0002153743,0.001190251,0.00007234686,0.005912473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00555306,"threshold_uncertainty_score":0.02272224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1806986612560722,"score_gpt":0.2435980985076099,"score_spread":0.06289943725153771,"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."}}