{"id":"W2130034541","doi":"","title":"Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing","year":2005,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Autoregressive model; Model selection; Monte Carlo method; Econometrics; Parametric statistics; Selection (genetic algorithm); Statistical hypothesis testing; Causality (physics); Inference; Sample (material); Computer science; Granger causality; Mathematics; Applied mathematics; Statistics; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00975131,0.0007145242,0.001717429,0.002219742,0.0006959716,0.001713121,0.001799158,0.001601903,0.004816964],"category_scores_gemma":[0.070787,0.0009563863,0.001559035,0.002158271,0.001740115,0.00181077,0.001463484,0.002400768,0.0005141429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266108,"about_ca_system_score_gemma":0.00222543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008041521,"about_ca_topic_score_gemma":0.007293371,"domain_scores_codex":[0.9937063,0.005082443,0.0001546344,0.0004329548,0.0004978881,0.0001259127],"domain_scores_gemma":[0.9138291,0.08044668,0.0017765,0.002344324,0.001283348,0.0003198716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001227301,0.00008907693,0.004840155,0.0001529855,0.0002073085,0.000127631,0.0001658298,0.772759,0.0004566266,0.1689562,0.000715321,0.05140717],"study_design_scores_gemma":[0.00002620643,0.00002390288,0.0003292621,0.00003014601,0.00001318138,0.00002342326,0.00001875157,0.9186231,0.0002129998,0.08007334,0.0006134157,0.00001239508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007216032,0.0003962848,0.9910656,0.0002038225,0.00003116593,0.00003150234,0.00005130886,0.0002685845,0.0007358117],"genre_scores_gemma":[0.5243196,0.001445732,0.4708403,0.000200632,0.0001718538,0.0005233695,0.0004093462,0.0001854118,0.001903794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00975131,"threshold_uncertainty_score":0.05157048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06255898422216441,"score_gpt":0.2480824692262285,"score_spread":0.1855234850040641,"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."}}