{"id":"W2091327810","doi":"10.1017/s0266466608080286","title":"MULTIVARIATE AUTOREGRESSION OF ORDER ONE WITH INFINITE VARIANCE INNOVATIONS","year":2008,"lang":"en","type":"article","venue":"Econometric Theory","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Autoregressive model; Mathematics; Consistency (knowledge bases); Independent and identically distributed random variables; Multivariate statistics; Econometrics; Domain (mathematical analysis); Stability (learning theory); Applied mathematics; Strong consistency; Vector autoregression; Limiting; Ordinary least squares; Least-squares function approximation; Statistics; Mathematical analysis; Computer science; Discrete mathematics","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":[0.0009103891,0.0001752799,0.0005199895,0.001169501,0.0001919954,0.00001654926,0.0002412428,0.0001227786,0.0007034044],"category_scores_gemma":[0.0007398142,0.0001833899,0.00006988191,0.002967005,0.0001581783,0.0003760265,0.00006466554,0.0002055499,0.0001946548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007426005,"about_ca_system_score_gemma":0.00007857957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000182086,"about_ca_topic_score_gemma":0.000005406408,"domain_scores_codex":[0.9983937,0.00002710485,0.0008433887,0.000412961,0.00004541907,0.0002774238],"domain_scores_gemma":[0.9984537,0.0002575124,0.0006259757,0.0004599766,0.0001375935,0.00006524382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001184945,0.0002751416,0.1145807,0.00003297924,0.00008111762,0.000002180966,0.000994252,0.001318233,0.00002053701,0.8797528,0.00005744175,0.002766041],"study_design_scores_gemma":[0.00232581,0.0003155612,0.7724795,0.00008976505,0.00001744851,0.00001135327,0.00008870394,0.01469005,0.0002494622,0.1964689,0.01252538,0.000738005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7632962,0.001742982,0.2154514,0.00009791808,0.0002339828,0.0002280412,0.0001421697,0.00005618035,0.0187511],"genre_scores_gemma":[0.9820409,0.0002315761,0.01644403,0.0000727989,0.00008273237,0.00002282296,0.00001994678,0.00003023873,0.001054912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6832839,"threshold_uncertainty_score":0.7701784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05471608161767527,"score_gpt":0.2267638480329046,"score_spread":0.1720477664152294,"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."}}