{"id":"W2477237354","doi":"10.1016/j.envres.2016.07.015","title":"Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets","year":2016,"lang":"en","type":"article","venue":"Environmental Research","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Econometrics; Heteroscedasticity; Autoregressive model; Autoregressive conditional heteroskedasticity; Vector autoregression; Volatility (finance); West Texas Intermediate; Stock (firearms); Markov chain; Mathematics; Economics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003170188,0.0009372954,0.00214845,0.00113494,0.0006470633,0.002067579,0.002144584,0.00194259,0.002601423],"category_scores_gemma":[0.00901284,0.001015833,0.001864804,0.002275306,0.001089799,0.002706151,0.000900641,0.002926064,0.0006491814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115184,"about_ca_system_score_gemma":0.001721978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01973275,"about_ca_topic_score_gemma":0.01413142,"domain_scores_codex":[0.9990293,0.0003973422,0.00005803327,0.0002380029,0.0001450704,0.0001323612],"domain_scores_gemma":[0.9954578,0.003006247,0.0005668601,0.000216381,0.0006263752,0.0001264015],"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":[0.0000826913,0.0000796537,0.003151663,0.00006200006,0.0001983728,0.0002602686,0.0001049744,0.8293757,0.0009025288,0.1477582,0.001758181,0.01626567],"study_design_scores_gemma":[0.000006008989,0.000009714201,0.0003393593,0.000003312844,0.0000242039,0.00001514563,0.000005620406,0.9932811,0.00007238315,0.006032148,0.0002002365,0.0000106362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060546,0.001170915,0.8875813,0.0007968486,0.0001992047,0.00005400225,0.0003668338,0.0004619081,0.003314449],"genre_scores_gemma":[0.9127782,0.002303149,0.06415775,0.0001679797,0.0002991075,0.0001866471,0.0009985388,0.0002447954,0.01886379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01973275,"threshold_uncertainty_score":0.03923577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247325025690922,"score_gpt":0.2707178783857731,"score_spread":0.2482446281288639,"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."}}