{"id":"W2169260562","doi":"10.1007/0-387-24555-3_14","title":"Optimal Detection of Periodicities in Vector Autoregressive Models","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Autoregressive model; Univariate; Mathematics; Asymptotic distribution; Context (archaeology); Applied mathematics; Covariance; Local asymptotic normality; Multivariate statistics; Residual; Autoregressive–moving-average model; Asymptotically optimal algorithm; Series (stratigraphy); Normality; Property (philosophy); Vector autoregression; Statistics; Mathematical optimization; Algorithm; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001573493,0.0007331658,0.001341206,0.0006106304,0.0002854235,0.001198163,0.000846719,0.001113004,0.00142086],"category_scores_gemma":[0.01047482,0.001141308,0.0005272764,0.0006653639,0.0005061296,0.001888722,0.001042485,0.001245246,0.0005060408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000313833,"about_ca_system_score_gemma":0.0007122915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670333,"about_ca_topic_score_gemma":0.001947678,"domain_scores_codex":[0.9993809,0.0002422487,0.00004840424,0.0001287995,0.0001366088,0.00006300611],"domain_scores_gemma":[0.9961898,0.003078403,0.000195151,0.0002293425,0.000232553,0.0000748282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003855229,0.0001132137,0.00218879,0.000235728,0.0001255585,0.0001598193,0.0001815242,0.381526,0.02105078,0.08661402,0.006014511,0.5014044],"study_design_scores_gemma":[0.000008810072,0.00001696444,0.000265792,0.000009091173,0.000006409739,0.00003011142,0.000007248613,0.9755217,0.00146789,0.02228771,0.0003700004,0.000008271501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01990942,0.0005027288,0.9782482,0.0001315124,0.00004216173,0.00001073904,0.00004768791,0.0003119968,0.0007954564],"genre_scores_gemma":[0.4126478,0.00117272,0.5807018,0.0001133394,0.0001912293,0.0000923887,0.0006983445,0.0003025492,0.004079893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001670333,"threshold_uncertainty_score":0.008321524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253636111408582,"score_gpt":0.2116774449946487,"score_spread":0.1691410838805629,"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."}}