{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001999398,0.0002459615,0.0007133111,0.0004529373,0.00004260987,0.00002192862,0.0001487403,0.0004148146,0.000555344],"category_scores_gemma":[0.00002462256,0.0002941765,0.0002017293,0.00002706723,0.00008568018,0.0002100832,0.00005102478,0.0003162109,0.00009273524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888876,"about_ca_system_score_gemma":0.00004149839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006043117,"about_ca_topic_score_gemma":0.0004598078,"domain_scores_codex":[0.9984462,0.000002885148,0.000897838,0.0004057091,0.00004663445,0.0002007064],"domain_scores_gemma":[0.9991591,0.00002671917,0.0004645014,0.0002643387,0.00004750229,0.00003786592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004443292,0.00002146405,0.0001262205,0.00005824639,0.00002707645,0.000002469727,0.001006977,0.01423897,0.000006988595,0.9793198,0.00002883328,0.005118499],"study_design_scores_gemma":[0.0007323388,0.0002009107,0.000742135,0.000295024,0.00001740573,0.000003133574,0.0000816133,0.6457912,0.000183042,0.30112,0.04988575,0.0009474746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02643508,0.01103522,0.03075731,0.00005628197,0.0004042386,0.0003491673,0.0003289163,0.00005469002,0.9305791],"genre_scores_gemma":[0.8351144,0.001128432,0.00134914,0.00002624195,0.0001885216,0.00001155622,0.00001509918,0.00004915733,0.1621175],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8086793,"threshold_uncertainty_score":0.9999511,"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."}}