{"id":"W3123691457","doi":"10.1080/07350015.2018.1497507","title":"Dynamic Effects of Credit Shocks in a Data-Rich Environment","year":2018,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université du Québec à Montréal","funders":"","keywords":"Counterfactual thinking; Economics; Recession; Shock (circulatory); Monetary economics; Great recession; Econometrics; Monetary policy; Interest rate; Identification (biology); Offset (computer science); Bond market; Real economy; Macroeconomics; Keynesian economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005653097,0.0001477157,0.0006488763,0.0003121437,0.00003753732,0.00003568162,0.0002963044,0.00007582965,0.0003847585],"category_scores_gemma":[0.0001389575,0.0001597168,0.00003096402,0.00005350465,0.0001486457,0.0003840523,0.000113276,0.0001182471,0.00009091145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001363723,"about_ca_system_score_gemma":0.0000375562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003863331,"about_ca_topic_score_gemma":0.0001168103,"domain_scores_codex":[0.9983493,0.00001276784,0.001155594,0.00023163,0.00002224791,0.0002284239],"domain_scores_gemma":[0.9983813,0.0001709704,0.001015818,0.0003291936,0.0000149658,0.00008773919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008544321,0.00102477,0.854413,0.001628774,0.001296276,0.0002035925,0.003948474,0.02375466,0.0001931989,0.04277279,0.03683836,0.03307167],"study_design_scores_gemma":[0.002566055,0.0003750017,0.8320827,0.00009556609,0.0000498359,0.00007969955,0.00006194865,0.1267905,0.00004494972,0.0249968,0.01247006,0.0003868584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666951,0.001651018,0.02902025,0.0001948987,0.0009752469,0.0001074973,0.0008775991,0.000002217584,0.000476212],"genre_scores_gemma":[0.9907955,0.001881283,0.006863866,0.00008217643,0.0002457814,0.000001225913,0.00002006902,0.00001856843,0.00009155806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1030359,"threshold_uncertainty_score":0.6513063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03865408083658074,"score_gpt":0.2351451906120306,"score_spread":0.1964911097754498,"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."}}