{"id":"W2586890965","doi":"10.5539/ibr.v10n3p57","title":"The Lead-Lag Relationship among East Asian Economies: A Wavelet Analysis","year":2017,"lang":"en","type":"article","venue":"International Business Research","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Composite index; Stock exchange; Lag; Economics; Index (typography); Financial crisis; Wavelet; Stock market; Granger causality; Stock market index; Econometrics; Robustness (evolution); Financial economics; Stock (firearms); Lead–lag compensator; Financial market; Finance; Macroeconomics; Geography; Computer science; Engineering","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":[0.001353515,0.0002566365,0.0002786647,0.00142971,0.0003196778,0.001169743,0.0002667852,0.0003171636,0.001643349],"category_scores_gemma":[0.003546546,0.0001758859,0.0005447979,0.001408625,0.0005108151,0.001294294,0.0007117036,0.0007189596,0.0001641388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347004,"about_ca_system_score_gemma":0.000369247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002645725,"about_ca_topic_score_gemma":0.00165064,"domain_scores_codex":[0.9997794,0.00006181705,0.00001648396,0.00005343731,0.00004602422,0.00004292425],"domain_scores_gemma":[0.9989311,0.0004367801,0.0002714216,0.00008958834,0.0001753108,0.00009587386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003205629,0.0002616082,0.5299019,0.0002347282,0.0007877477,0.003653952,0.003553579,0.07003502,0.008487004,0.2134265,0.00399709,0.1653403],"study_design_scores_gemma":[0.0000402803,0.0001733772,0.3175,0.00008203166,0.0003673679,0.000520116,0.002279091,0.5818554,0.001924698,0.0884397,0.006739692,0.00007835343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.920728,0.001059897,0.07384,0.0005150872,0.00004044719,0.00002215579,0.0001291826,0.00003541745,0.003629686],"genre_scores_gemma":[0.9938083,0.0007071607,0.004388928,0.00002860339,0.00002935896,0.00001062367,0.00008207151,0.000008101666,0.000936766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002645725,"threshold_uncertainty_score":0.00715816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171708940305217,"score_gpt":0.3453435539255875,"score_spread":0.2281726598950658,"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."}}