{"id":"W2470743635","doi":"10.2139/ssrn.2806403","title":"Macroeconomic Stress-Testing of Mortgage Default Rate Using a Vector Error Correction Model and Entropy Pooling","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Bank of Canada; Bank of Canada; Université Laval; HEC Montréal","funders":"","keywords":"Pooling; Download; Computer science; Econometrics; Stress testing (software); Operating system; Economics; Artificial intelligence","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":[0.009326502,0.0008326204,0.001411739,0.0009439254,0.0005280385,0.001486806,0.00135692,0.001297674,0.002326052],"category_scores_gemma":[0.02465386,0.0005366018,0.001438591,0.0006830801,0.0008201806,0.002035037,0.00130382,0.001039366,0.000281551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004747513,"about_ca_system_score_gemma":0.0009751154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009374723,"about_ca_topic_score_gemma":0.004949352,"domain_scores_codex":[0.9971825,0.00165956,0.0001842187,0.0005734222,0.0001699524,0.0002303516],"domain_scores_gemma":[0.9777475,0.01815938,0.001321801,0.001346802,0.0009300152,0.0004946399],"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":[0.001695499,0.0004175119,0.1339099,0.0001132674,0.001039137,0.0006181057,0.0003470567,0.7872834,0.003008156,0.01397053,0.0009745948,0.05662295],"study_design_scores_gemma":[0.0000218251,0.00008235678,0.01121799,0.00000423343,0.00005375965,0.00002624533,0.00002311166,0.9854975,0.0006028831,0.002414037,0.00003995235,0.00001610232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8460444,0.0001171153,0.1522366,0.0002465904,0.0000497545,0.00003300344,0.0002334854,0.0002793513,0.0007597263],"genre_scores_gemma":[0.9946364,0.00001907848,0.004556715,0.00001483867,0.00001886425,0.00001105538,0.0002097486,0.00001540043,0.0005179557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009374723,"threshold_uncertainty_score":0.04932386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02847401208244297,"score_gpt":0.2287243532585213,"score_spread":0.2002503411760783,"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."}}