{"id":"W3121784800","doi":"","title":"Coordinating expectations through central bank projections","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"","keywords":"Rational expectations; Inflation (cosmology); Economics; Dual (grammatical number); Interest rate; Central bank; Monetary policy; Adaptive expectations; Inflation targeting; Forward guidance; Real interest rate; Adaptive learning; Ex-ante; Econometrics; Monetary economics; Macroeconomics; Computer science; Credit channel","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":[0.004770943,0.0007645579,0.0005306097,0.0001822286,0.0003351066,0.002140214,0.0005575066,0.0008767929,0.004615621],"category_scores_gemma":[0.0303257,0.0004256137,0.0002377391,0.0002549532,0.0007141188,0.001593833,0.001241084,0.001526269,0.0009861952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005743034,"about_ca_system_score_gemma":0.001219374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006187072,"about_ca_topic_score_gemma":0.0003333334,"domain_scores_codex":[0.9965478,0.002098808,0.0001666511,0.0006012631,0.0004028851,0.0001826956],"domain_scores_gemma":[0.9859331,0.00779604,0.00282942,0.002013805,0.0008855488,0.0005419923],"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":[0.01309334,0.00243669,0.05070521,0.000610876,0.0004658259,0.0004613236,0.004622178,0.3273239,0.1805412,0.190277,0.006592398,0.2228701],"study_design_scores_gemma":[0.002107711,0.004182644,0.03351717,0.0001865469,0.0004012605,0.0001855908,0.0009937347,0.6076836,0.06224253,0.2742461,0.01398105,0.0002720671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8388335,0.000182727,0.1419068,0.0010244,0.0001394979,0.0001943926,0.0003015311,0.0007218362,0.01669547],"genre_scores_gemma":[0.9893513,0.00004864268,0.009335673,0.0001110701,0.00002139891,0.0001238153,0.00005721209,0.00003474871,0.0009162118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004770943,"threshold_uncertainty_score":0.02523148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08207685892453995,"score_gpt":0.3263957271949918,"score_spread":0.2443188682704518,"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."}}