{"id":"W3211766101","doi":"10.37625/abr.24.2.62-99","title":"Financial Frictions and Macroeconomy During Financial Crises: A Bayesian DSGE Assessment","year":2021,"lang":"en","type":"article","venue":"American Business Review","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dynamic stochastic general equilibrium; Variance decomposition of forecast errors; Economics; Financial accelerator; Financial crisis; Business cycle; Finance; Investment (military); Monetary economics; Macroeconomics; Monetary policy; Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000252894,0.0002748581,0.001037076,0.0001476842,0.0002651939,0.00009412508,0.00016943,0.00005489693,0.001363723],"category_scores_gemma":[0.0002944674,0.0003305224,0.0001676491,0.000684272,0.0001545279,0.0003829203,0.0001210597,0.0001965211,0.0002595807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001386561,"about_ca_system_score_gemma":0.0001488611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000966878,"about_ca_topic_score_gemma":0.00008338389,"domain_scores_codex":[0.9979828,0.00003605649,0.0008568884,0.0006325843,0.00002987657,0.0004618107],"domain_scores_gemma":[0.9987704,0.00004063029,0.0005220803,0.0004836449,0.00004030281,0.000142941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008928005,0.001353506,0.475635,0.01525456,0.0006778942,0.0005780514,0.0009410818,0.0007657167,0.00009427008,0.1870795,0.06380582,0.2537253],"study_design_scores_gemma":[0.0005025294,0.00003624322,0.6279594,0.0005144709,0.00004912948,0.0001457878,0.00002647685,0.0003720623,0.000009043208,0.002661434,0.3671075,0.000615846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7252787,0.2035546,0.01352495,0.02230901,0.001363296,0.001223233,0.0009652854,0.0001858748,0.03159501],"genre_scores_gemma":[0.8234147,0.1645784,0.003112867,0.007385243,0.0004483353,0.0001501275,0.00007256719,0.00004840097,0.0007894029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3033017,"threshold_uncertainty_score":0.9999147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03728623174515042,"score_gpt":0.2603419256646166,"score_spread":0.2230556939194661,"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."}}