{"id":"W2461533408","doi":"","title":"Measuring Eective Monetary Policy Conservatism of Central Banks: A Dynamic Approach","year":2016,"lang":"en","type":"article","venue":"Annals of economics and finance","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conservatism; Monetary policy; Economics; Variance (accounting); Logit; Output gap; Central bank; Monetary economics; Econometrics; Ordered logit; Panel data; Accounting; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003764021,0.0004592107,0.0006878984,0.001739161,0.000286475,0.002086494,0.001023235,0.0009730648,0.001508281],"category_scores_gemma":[0.01863256,0.0004240589,0.000666505,0.002252742,0.0007276522,0.001501972,0.001253838,0.0009616096,0.0001497053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151937,"about_ca_system_score_gemma":0.0007373709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004577726,"about_ca_topic_score_gemma":0.002714124,"domain_scores_codex":[0.9981488,0.0008504969,0.0001735155,0.0004453173,0.0002444561,0.0001375185],"domain_scores_gemma":[0.9787027,0.01047637,0.008227861,0.001558323,0.0007577316,0.0002769701],"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":[0.0006723646,0.0003067929,0.3387587,0.0001125024,0.0009665127,0.0004612484,0.0009013516,0.5939948,0.004425996,0.02701512,0.0005023276,0.03188224],"study_design_scores_gemma":[0.00005341967,0.000286818,0.1331982,0.00002601541,0.0001203137,0.0002120152,0.0004049151,0.8389418,0.001276991,0.02451876,0.0008875508,0.00007312524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435596,0.0001234276,0.05441213,0.0001824592,0.000007991417,0.00002777949,0.0002892222,0.00004808648,0.001349344],"genre_scores_gemma":[0.994844,0.00004050011,0.004638545,0.00001593477,0.00001052425,0.00001526705,0.0001844193,0.000004577315,0.0002462065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004577726,"threshold_uncertainty_score":0.01990628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1237785098486176,"score_gpt":0.2384085465690645,"score_spread":0.1146300367204469,"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."}}