{"id":"W1762321683","doi":"","title":"Applications of Bayesian Econometrics to Financial Economics","year":2005,"lang":"en","type":"dissertation","venue":"Lund University Publications (Lund University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Markov chain Monte Carlo; Portfolio; Estimator; Bayesian probability; Bayesian econometrics; Shrinkage estimator; Economics; Computer science; Bayesian inference; Statistics; Finance; Mathematics; Bayesian statistics; Bias of an estimator; Minimum-variance unbiased estimator","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004169811,0.0003225702,0.0004677027,0.005433227,0.001718594,0.0001393076,0.001900526,0.000522212,0.0004435483],"category_scores_gemma":[0.00009752362,0.0005155841,0.0003759333,0.006252854,0.0002986023,0.0009692438,0.0001694129,0.0003528555,0.00009669084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382874,"about_ca_system_score_gemma":0.001389693,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002437572,"about_ca_topic_score_gemma":0.06984132,"domain_scores_codex":[0.9977939,0.0001723789,0.000372331,0.0007958583,0.0003323103,0.0005332662],"domain_scores_gemma":[0.9971333,0.0001425917,0.0006418789,0.0008563407,0.000790636,0.0004353141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004026196,0.0002587804,0.001532678,0.00004676851,0.0001246082,0.000002102215,0.001999347,0.0001156096,8.088325e-7,0.9826608,0.004028502,0.009189714],"study_design_scores_gemma":[0.0003381865,0.00002801758,0.008509138,0.00001769952,0.0002611885,1.618888e-7,0.01510292,0.00001946787,0.000003488457,0.0006234453,0.9746338,0.0004625508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.008401397,0.00004815594,0.004584902,0.001230286,0.0003751427,0.001435686,0.0005011411,0.0001838521,0.9832394],"genre_scores_gemma":[0.01257876,0.002022039,0.002774567,0.0001125882,0.0003335155,0.000006572342,0.001298049,0.00004192452,0.980832],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9820374,"threshold_uncertainty_score":0.9997296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236971716085966,"score_gpt":0.2350436705386449,"score_spread":0.2226739533777852,"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."}}