{"id":"W4388542675","doi":"10.3982/qe1533","title":"Behavioral learning equilibria in New Keynesian models","year":2023,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"China Scholarship Council; De Nederlandsche Bank; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Bank of Canada","keywords":"New Keynesian economics; Rational expectations; Benchmark (surveying); Econometrics; Inflation (cosmology); Phillips curve; Autocorrelation; Sample (material); Smoothing; Computer science; Monetary policy; Economics; Mathematics; Statistics; Keynesian economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007108537,0.0002523032,0.0006061425,0.0006884107,0.00009691097,0.0001162491,0.0003003165,0.0001482849,0.000428989],"category_scores_gemma":[0.00006904473,0.0003505583,0.0001672365,0.0003181316,0.00006581432,0.00106061,0.00009511424,0.0002888623,0.006648486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131882,"about_ca_system_score_gemma":0.00005933599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00269224,"about_ca_topic_score_gemma":0.0004856519,"domain_scores_codex":[0.9977985,0.00002975134,0.0009009652,0.0005960608,0.00001477555,0.0006599735],"domain_scores_gemma":[0.9990262,0.0001257185,0.0003451772,0.0003064494,0.000004052906,0.0001924129],"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":[0.00007579157,0.000066313,0.06669001,0.00001516265,0.00004911421,0.00001277821,0.004767329,0.2726251,0.00002072625,0.651412,0.002998732,0.001267043],"study_design_scores_gemma":[0.0008873193,0.0001677182,0.01194846,0.00001133069,0.000003831231,0.000002888582,0.0006095532,0.7367293,0.00001681437,0.2357334,0.01342505,0.0004643496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786063,0.0003393578,0.001337013,0.0008004316,0.0004617329,0.0002084977,0.00009459435,0.0001271251,0.01802497],"genre_scores_gemma":[0.9914255,0.0004685004,0.001650745,0.0001632241,0.0001240849,0.00001494809,0.00009523251,0.00006322178,0.005994583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4641043,"threshold_uncertainty_score":0.9998946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3020493883405529,"score_gpt":0.3131562873276906,"score_spread":0.01110689898713768,"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."}}