{"id":"W2034133493","doi":"10.2307/3316087","title":"On testing for multivariate ARCH effects in vector time series models","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; HEC Montréal; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Mathematics; Estimator; Multivariate statistics; Smoothing; Applied mathematics; Residual; Arch; Statistics; Multivariate kernel density estimation; Weighting; Series (stratigraphy); Kernel smoother; Kernel density estimation; Kernel method; Algorithm; Variable kernel density estimation; Computer science; Engineering; Support vector machine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007676362,0.00009992491,0.0003354796,0.0003053753,0.00009012064,0.00004434415,0.0001028927,0.0000598096,0.00002594636],"category_scores_gemma":[0.004512927,0.0001151156,0.00004383537,0.0001406488,0.00002945092,0.0001370049,0.000002556203,0.0001774072,0.00001718599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001920699,"about_ca_system_score_gemma":0.0003574609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002778841,"about_ca_topic_score_gemma":0.00382055,"domain_scores_codex":[0.998916,0.00002295359,0.0006159484,0.0001313891,0.00002840143,0.0002853216],"domain_scores_gemma":[0.9987486,0.00054122,0.0002923074,0.00009797643,0.000122628,0.0001972731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000266909,0.00001848519,0.01319367,0.00005144386,0.00001372797,0.00005109057,0.0005925046,0.01956068,0.00001257016,0.9643753,0.0006493125,0.001454574],"study_design_scores_gemma":[0.0007593191,0.0004009512,0.01343496,0.00009632963,0.000005444075,0.000011084,0.00001691195,0.1831269,0.00002449671,0.8004721,0.001468508,0.0001829919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866007,0.0005278279,0.8079935,0.00009520822,0.0006557455,0.0002926406,0.001203508,0.000003612731,0.002627252],"genre_scores_gemma":[0.8700508,0.000004377367,0.1296729,0.00006153645,0.00004411842,0.000003544446,0.000004046432,0.00001851836,0.0001402038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6834501,"threshold_uncertainty_score":0.5402721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04953210908629847,"score_gpt":0.226875821132608,"score_spread":0.1773437120463096,"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."}}