{"id":"W2972890194","doi":"10.2139/ssrn.3450570","title":"Behavioral Learning Equilibria in the New Keynesian Model","year":2019,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Economic Policies and Impacts","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada; Government of Canada","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"New Keynesian economics; Stability (learning theory); Rational expectations; Econometrics; Monetary policy; Economics; Bayesian probability; Autocorrelation; Sample (material); Simple (philosophy); Mathematical economics; Computer science; Mathematics; Statistics; Keynesian economics; Physics","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":[0.001039101,0.0003476884,0.001086606,0.0004508899,0.0005544773,0.002335907,0.0009154498,0.001641617,0.01118409],"category_scores_gemma":[0.004860751,0.0002778396,0.0005633748,0.0004409258,0.001520883,0.003193117,0.001209474,0.001593819,0.000791237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376207,"about_ca_system_score_gemma":0.0008408236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002962561,"about_ca_topic_score_gemma":0.001937915,"domain_scores_codex":[0.9996421,0.0001487098,0.00001506953,0.00006024115,0.00005738906,0.00007649595],"domain_scores_gemma":[0.9983754,0.000921349,0.0002909435,0.0001187842,0.0001064896,0.0001869577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008598925,0.00006462853,0.0006513571,0.00004886333,0.00002721839,0.00009702559,0.0001364717,0.05925535,0.0005267807,0.93291,0.001709358,0.004487079],"study_design_scores_gemma":[0.0001000766,0.00002701142,0.0003330267,0.0000129639,0.00001102101,0.00004463039,0.00006565631,0.2299244,0.0001169557,0.7680703,0.001275632,0.00001825289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.617115,0.00099224,0.2635993,0.01082797,0.0002177924,0.000107408,0.0005785864,0.0002722583,0.1062893],"genre_scores_gemma":[0.9714475,0.0004708752,0.005694648,0.0002657285,0.00009138792,0.00005489845,0.00006664629,0.000023447,0.02188493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01118409,"threshold_uncertainty_score":0.03741455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302743324842388,"score_gpt":0.2656325524004295,"score_spread":0.2226051191520056,"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."}}