{"id":"W2753419073","doi":"","title":"Crude Oil Price Volatility Spillovers and Agricultural Commodities: A Study in Time and Frequency Domains","year":2017,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bivariate analysis; Cointegration; Economics; Econometrics; Volatility (finance); Crude oil; Granger causality; Agriculture; Oil price; Monetary economics; Mathematics; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002609451,0.0001357014,0.0002565834,0.001055744,0.0001122261,0.0005515736,0.0001211751,0.0001841019,0.0008700698],"category_scores_gemma":[0.001167676,0.0000878912,0.0002754322,0.001661874,0.000152308,0.0007663972,0.0002814772,0.0002280811,0.0001070769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001850819,"about_ca_system_score_gemma":0.0001986324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001902623,"about_ca_topic_score_gemma":0.001189891,"domain_scores_codex":[0.9998947,0.000019659,0.000005869066,0.00002390929,0.00003977753,0.0000159817],"domain_scores_gemma":[0.9992777,0.0003880257,0.0002032541,0.00003378338,0.00007188292,0.00002550818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006677461,0.0004856224,0.618713,0.0008653218,0.0008586345,0.003228663,0.002352546,0.018732,0.02352909,0.04571399,0.001689431,0.283164],"study_design_scores_gemma":[0.00001391061,0.0001992935,0.9531253,0.0001229351,0.0002687409,0.001514893,0.001190269,0.02355506,0.002549655,0.00981051,0.007608798,0.00004060834],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836811,0.007924099,0.003439302,0.0001798857,0.00001556653,0.00000733119,0.0001150625,0.00001150772,0.004626084],"genre_scores_gemma":[0.9940106,0.004563461,0.0006225159,0.00002576687,0.00005801236,0.000003119349,0.0001075145,0.000003306474,0.0006056727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001902623,"threshold_uncertainty_score":0.003783107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979731897631041,"score_gpt":0.2309558985674262,"score_spread":0.2111585795911158,"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."}}