{"id":"W3125895547","doi":"","title":"Can Dynamic Panel Data Explain the Finance-Growth Link? An Empirical Likelihood Approach","year":2005,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic Growth and Development","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Panel data; Estimator; Generalized method of moments; Econometrics; Economics; Moment (physics); Intermediation; Sample (material); Estimation; Financial intermediary; Maximum likelihood; Empirical likelihood; Statistics; Mathematics; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.005110926,0.0005684244,0.0007389011,0.0005189361,0.0004234863,0.0008278417,0.01115976,0.0006067565,0.00001684117],"category_scores_gemma":[0.0003190229,0.0005049533,0.0001452019,0.0002907184,0.0003767877,0.0005756952,0.01191086,0.003042879,0.00004036153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001489445,"about_ca_system_score_gemma":0.003241484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001804671,"about_ca_topic_score_gemma":0.001317174,"domain_scores_codex":[0.9933257,0.0006305036,0.001109723,0.002882732,0.0004178004,0.001633619],"domain_scores_gemma":[0.9923829,0.000569946,0.0003079498,0.006191037,0.000126719,0.0004214195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004944844,0.0004963478,0.005974405,0.0001446283,0.0001357589,0.00005670623,0.003941779,0.007525977,0.000005980923,0.003316611,0.0009439295,0.9774084],"study_design_scores_gemma":[0.0007399925,0.0001000975,0.02244019,0.00008994912,0.00000764074,0.00005487382,0.000558976,0.9398262,0.00002321718,0.0146197,0.02053565,0.001003506],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.722012,0.002743924,0.00988817,0.06343278,0.004646442,0.008188596,0.001170214,0.000933771,0.1869841],"genre_scores_gemma":[0.8950481,0.0165886,0.08297197,0.001475746,0.0008811855,0.0007356163,0.0009983899,0.000130922,0.001169453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9764049,"threshold_uncertainty_score":0.9997402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07949483842972409,"score_gpt":0.3280913832761535,"score_spread":0.2485965448464294,"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."}}