{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002266781,0.000149688,0.0003056774,0.0002002994,0.0002450173,0.00004790583,0.000207326,0.00007119197,0.00003272383],"category_scores_gemma":[0.0002222231,0.0001826197,0.00001604801,0.0002148104,0.00007713116,0.0007878064,0.0001169821,0.00009605666,0.000007948268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630723,"about_ca_system_score_gemma":0.00004229402,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04372281,"about_ca_topic_score_gemma":0.01828204,"domain_scores_codex":[0.9988219,0.00002425013,0.0003593044,0.000542591,0.00004132224,0.0002106228],"domain_scores_gemma":[0.9981991,0.0008710446,0.0003083453,0.000441834,0.00004272397,0.0001369485],"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":[0.00008925612,0.0000911564,0.179608,0.00003247377,0.00004893578,0.000003657829,0.0001236253,0.8159685,0.000007079223,0.001360458,0.00007547388,0.002591484],"study_design_scores_gemma":[0.001095726,0.00009513488,0.03054892,0.00003320235,0.00003058275,0.000002120149,0.00005791746,0.9522125,0.00001125233,0.001137956,0.01451154,0.00026319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.703505,0.0004332291,0.2900115,0.0007811299,0.00004099624,0.0008145819,0.001556757,0.00005500277,0.002801738],"genre_scores_gemma":[0.9877556,0.0000851396,0.01171564,0.00008500299,0.00003677764,0.000002998735,0.0001160191,0.000008992608,0.0001938376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2842505,"threshold_uncertainty_score":0.9996318,"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."}}