{"id":"W4386927625","doi":"10.7202/1091505ar","title":"MACROECONOMIC STRESS-TESTING OF MORTGAGE DEFAULT RATE USING A VECTOR ERROR CORRECTION MODEL AND ENTROPY POOLING","year":2016,"lang":"en","type":"article","venue":"Assurances et gestion des risques","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pooling; Econometrics; Stress test; Stress testing (software); Error correction model; Computer science; Economics; Artificial intelligence; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004180497,0.0001444596,0.0003115661,0.0001782974,0.0001574225,0.00005006219,0.00007741291,0.00009142204,0.00002206283],"category_scores_gemma":[0.0005690833,0.000138562,0.00005624444,0.0001516637,0.0001468228,0.0005656896,0.00002909716,0.00008341797,0.00001089438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128058,"about_ca_system_score_gemma":0.00003292751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007627864,"about_ca_topic_score_gemma":0.0003546106,"domain_scores_codex":[0.9988979,0.00003253004,0.0005210689,0.000311886,0.00002962034,0.000207018],"domain_scores_gemma":[0.9990154,0.0002620707,0.000437032,0.0001492792,0.00008197282,0.00005427196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008279573,0.00007604811,0.6232014,0.00007770256,0.00006060945,0.000002222526,0.0006323926,0.3132288,0.008874788,0.02377669,0.0001273849,0.02985916],"study_design_scores_gemma":[0.0003036072,0.00004576375,0.4117115,0.0001608212,0.00001263098,0.000005010595,0.00003147496,0.573328,0.001389944,0.01275072,0.00009104881,0.0001695043],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9429461,0.000640991,0.05466151,0.0002404703,0.0003448754,0.0001277266,0.0002339877,0.00005372932,0.0007505985],"genre_scores_gemma":[0.9928235,0.000555762,0.006242497,0.000007683676,0.00008987331,0.00001299602,0.000007116304,0.00002200674,0.0002385106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2600992,"threshold_uncertainty_score":0.5650395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0752483633025168,"score_gpt":0.2762964253999368,"score_spread":0.20104806209742,"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."}}