{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000198612,0.0001088931,0.0004371278,0.0001142546,0.00004310098,0.00005742834,0.00008997574,0.00005734554,0.000129219],"category_scores_gemma":[0.0002591381,0.0001239749,0.0000323017,0.00007459577,0.00004729781,0.0006505911,0.00003470093,0.0001643693,0.00005251096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007336328,"about_ca_system_score_gemma":0.00007883261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001463987,"about_ca_topic_score_gemma":0.00002956273,"domain_scores_codex":[0.998904,0.000007710926,0.0007678826,0.0001588621,0.00002401244,0.0001374789],"domain_scores_gemma":[0.9991703,0.0001317657,0.0004900502,0.00005473197,0.00006995161,0.00008319815],"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":[0.0006095914,0.00009136058,0.07328505,0.0001111253,0.00007245489,0.00004414819,0.001583475,0.6033729,0.00001934883,0.3160153,0.0008608454,0.003934362],"study_design_scores_gemma":[0.0005183436,0.000116963,0.04719852,0.00002909695,0.000006032807,0.0000104418,0.00004707151,0.8170031,0.000001933327,0.1343961,0.0005184263,0.0001539708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2009718,0.0006110811,0.7965885,0.000599874,0.000271223,0.00005730355,0.0004500464,0.000004591262,0.0004456747],"genre_scores_gemma":[0.9859005,0.0006893001,0.01308201,0.0001231313,0.0001630237,0.000001199473,0.000004816671,0.00001174193,0.00002434525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7849287,"threshold_uncertainty_score":0.5055548,"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."}}