{"id":"W1514590158","doi":"10.3386/w10447","title":"A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1","year":2004,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Jackknife resampling; Estimator; Statistics; Variance (accounting); Mathematics; Econometrics; Minimum-variance unbiased estimator; Tracking error; Computer science; Economics; Artificial intelligence; Accounting","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":[0.01631089,0.001118752,0.002005009,0.002387096,0.0008029879,0.001749158,0.002314768,0.001864022,0.002335636],"category_scores_gemma":[0.1094558,0.001035392,0.001077766,0.002001158,0.001551454,0.003074729,0.001544052,0.002565788,0.0009869683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255954,"about_ca_system_score_gemma":0.002119055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005475914,"about_ca_topic_score_gemma":0.005405959,"domain_scores_codex":[0.9919057,0.003682142,0.0005154752,0.001496341,0.001976047,0.0004242606],"domain_scores_gemma":[0.9493651,0.03514204,0.004701562,0.005336296,0.005021305,0.0004337743],"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":[0.0003896102,0.0002198176,0.03184533,0.0002422455,0.0007262631,0.0001899654,0.00032168,0.6478764,0.004275831,0.06320474,0.00543775,0.2452703],"study_design_scores_gemma":[0.00006278951,0.0001293037,0.008502794,0.0001295195,0.00007394548,0.0002127035,0.00005030674,0.9453138,0.004862898,0.03776162,0.002814183,0.00008616944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00988874,0.00009724049,0.9890454,0.00005409853,0.0000170097,0.00004888085,0.0001205618,0.0002887081,0.0004393275],"genre_scores_gemma":[0.279607,0.0003033415,0.7151289,0.000240176,0.0001050551,0.0004681374,0.001667156,0.000304853,0.002175343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01631089,"threshold_uncertainty_score":0.08626121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.288630631833755,"score_gpt":0.4589883131717963,"score_spread":0.1703576813380413,"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."}}