{"id":"W3122428556","doi":"10.5430/ijfr.v12n2p263","title":"Financial Deepening and Economic Growth in Nigeria: A Johannsen and Error Correction Model Techniques","year":2021,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Covenant University Centre for Research, Innovation and Discovery; Covenant University","keywords":"Cointegration; Nexus (standard); Error correction model; Granger causality; Economics; Proxy (statistics); Stock market; Financial deepening; Gross domestic product; Financial system; Stock exchange; Finance; Financial market; Macroeconomics; Econometrics; Financial intermediary; Statistics; Mathematics","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":[0.005029741,0.0008795194,0.001150304,0.001394179,0.0008064171,0.002441227,0.0008510627,0.001089092,0.002253311],"category_scores_gemma":[0.007747409,0.0004909594,0.001227699,0.001481794,0.0008805165,0.001286103,0.001229446,0.002222907,0.0004121943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007377543,"about_ca_system_score_gemma":0.002735502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02669627,"about_ca_topic_score_gemma":0.01846014,"domain_scores_codex":[0.9982552,0.00104942,0.0001007466,0.0002176743,0.0001848745,0.0001919502],"domain_scores_gemma":[0.9929669,0.005713974,0.0005665261,0.0002343576,0.0004034847,0.0001147728],"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":[0.0007133745,0.0008039325,0.2784216,0.0005120214,0.00167732,0.00316724,0.002983256,0.4951613,0.002546969,0.1073043,0.004524181,0.1021845],"study_design_scores_gemma":[0.00005295411,0.0003470508,0.02363394,0.0002003868,0.0003656964,0.00018367,0.0008705863,0.9543657,0.0008593337,0.01423976,0.004823279,0.00005780785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7903368,0.003398188,0.1931085,0.002761481,0.0004507663,0.000203195,0.000675225,0.0003797438,0.008686092],"genre_scores_gemma":[0.9772205,0.001454468,0.01361688,0.00008659203,0.00007873128,0.0001310029,0.0003505757,0.0000342912,0.00702695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02669627,"threshold_uncertainty_score":0.05308175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1082680844600932,"score_gpt":0.3425727762323212,"score_spread":0.234304691772228,"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."}}