{"id":"W3131648018","doi":"10.2139/ssrn.3226570","title":"Stationary Threshold Vector Autoregressive Models","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Western University","funders":"","keywords":"Autoregressive model; Nonlinear autoregressive exogenous model; SETAR; STAR model; Econometrics; Mathematics; Computer science; Applied mathematics; Autoregressive integrated moving average; Statistics; Time series","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":[],"consensus_categories":[],"category_scores_codex":[0.001207166,0.0001455085,0.0002483864,0.0001710708,0.0003470832,0.00006497373,0.0002656597,0.00009849928,0.0001267838],"category_scores_gemma":[0.00006506732,0.0001551239,0.0001237618,0.0001391303,0.00007650977,0.000516729,0.00003681434,0.0009214426,0.0003165488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006192419,"about_ca_system_score_gemma":0.0005729096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001332324,"about_ca_topic_score_gemma":0.0002866683,"domain_scores_codex":[0.9976683,0.00001207219,0.0005007758,0.000286565,0.00006616714,0.001466067],"domain_scores_gemma":[0.9992978,0.00002091264,0.0002983799,0.0001987193,0.0001075377,0.00007663111],"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":[0.00003822517,0.00003631131,0.003379736,0.000002306572,0.00004836289,0.00000126868,0.0005076051,0.0003865055,0.000008717348,0.99243,0.0002086911,0.002952213],"study_design_scores_gemma":[0.000348394,0.0002172624,0.001307287,0.00001003012,0.00000460391,0.00003265306,0.0001841597,0.08088598,0.00001468202,0.9142774,0.002542508,0.0001750156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5889417,0.01616556,0.3709246,0.00110444,0.001017885,0.0001891701,0.00005342376,0.00007225083,0.02153101],"genre_scores_gemma":[0.9949586,0.001977996,0.0005066007,0.0001285805,0.0008032587,0.000005264231,0.000005742365,0.0000276641,0.001586289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4060169,"threshold_uncertainty_score":0.6325768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03077242603238437,"score_gpt":0.2375287483691593,"score_spread":0.206756322336775,"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."}}