{"id":"W2791200162","doi":"10.1080/07350015.2020.1773275","title":"Adaptive Inference in Heteroscedastic Fractional Time Series Models","year":2020,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Università di Bologna; Social Sciences and Humanities Research Council of Canada; Canada Research Chairs","keywords":"Heteroscedasticity; Series (stratigraphy); Inference; Econometrics; Mathematics; Computer science; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006810521,0.0007216659,0.001281797,0.0009896929,0.0004510777,0.001195401,0.001785823,0.00149305,0.001029062],"category_scores_gemma":[0.03393156,0.0005129273,0.0008738351,0.00135256,0.002151553,0.001890476,0.001543246,0.001613413,0.000109289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009813411,"about_ca_system_score_gemma":0.001012587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00396545,"about_ca_topic_score_gemma":0.002572659,"domain_scores_codex":[0.9976209,0.001303115,0.00009846882,0.0004541035,0.0003760883,0.0001473914],"domain_scores_gemma":[0.9819516,0.01515307,0.00166037,0.0007026151,0.0004032402,0.0001291198],"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":[0.0000670133,0.00005423829,0.003156152,0.00008205147,0.0001330561,0.0002729308,0.0001235977,0.759982,0.00140997,0.2058879,0.0003309845,0.02850019],"study_design_scores_gemma":[0.00000614845,0.00001245715,0.0002697897,0.000005542418,0.000008917517,0.00001549575,0.000009259884,0.9249336,0.0002523538,0.07430588,0.0001729893,0.00000766865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03116171,0.0002475398,0.9676557,0.0002249811,0.00002725069,0.00001243224,0.00003290782,0.00007934645,0.0005581616],"genre_scores_gemma":[0.8581449,0.0006623231,0.1393116,0.0001401957,0.0001447148,0.00007965568,0.0001072905,0.00004027478,0.00136904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006810521,"threshold_uncertainty_score":0.03601789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06035530149714532,"score_gpt":0.2339707010104974,"score_spread":0.1736153995133521,"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."}}