{"id":"W4251337155","doi":"10.3390/jrfm13110257","title":"Bayesian Econometrics","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Bayesian econometrics; Bayesian probability; Financial econometrics; Economics; Econometrics; Macro; Key (lock); Field (mathematics); Bayesian vector autoregression; Computer science; Bayesian statistics; Financial market; Bayesian inference; Finance; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0040813,0.0009171842,0.001457664,0.002208255,0.0007763625,0.003743658,0.001560248,0.002649545,0.01505671],"category_scores_gemma":[0.02456805,0.0007521004,0.001255768,0.001870953,0.002182778,0.003505124,0.00174502,0.002670475,0.003688436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001978678,"about_ca_system_score_gemma":0.001630349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008457095,"about_ca_topic_score_gemma":0.003855565,"domain_scores_codex":[0.9974977,0.001200697,0.0001122507,0.000378387,0.0006414495,0.0001694553],"domain_scores_gemma":[0.9920785,0.005830126,0.0004510143,0.0006455792,0.0008072885,0.0001874559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001430514,0.00001789842,0.000807775,0.00008643302,0.00005249412,0.00004504187,0.00005617748,0.04865659,0.0001380927,0.9158291,0.005977288,0.02831871],"study_design_scores_gemma":[0.00001490811,0.000009967917,0.0004693855,0.00005353681,0.00001617473,0.00007409206,0.0000219783,0.1347146,0.0000764496,0.8490794,0.01544766,0.00002188699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004856785,0.005153758,0.9362926,0.005294275,0.0005038288,0.00006138494,0.0006700394,0.0003951698,0.04677221],"genre_scores_gemma":[0.6089614,0.02553628,0.28861,0.003651204,0.003394148,0.0005659476,0.002285252,0.0004855091,0.06651013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01505671,"threshold_uncertainty_score":0.05036968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04370673817544385,"score_gpt":0.1972313551290468,"score_spread":0.153524616953603,"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."}}