{"id":"W3199266254","doi":"10.1093/jjfinec/nbad031","title":"Composite Likelihood for Stochastic Migration Model with Unobserved Factor","year":2023,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Econometrics; Mathematics; Probit; Probit model; Likelihood function; Ordered probit; Consistency (knowledge bases); Credit risk; Asymptotic distribution; Statistics; Economics; Applied mathematics; Actuarial science; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006296052,0.0002010909,0.0006570969,0.001835105,0.0001969863,0.0001040701,0.0002885543,0.0001595241,0.00002501808],"category_scores_gemma":[0.0007976218,0.0002072488,0.00030426,0.00194549,0.00004809662,0.0005226365,0.00003243138,0.0002108841,0.00007377291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002058259,"about_ca_system_score_gemma":0.0002239178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002060515,"about_ca_topic_score_gemma":0.000071305,"domain_scores_codex":[0.9980701,0.000006403862,0.001168074,0.0002692629,0.0000839845,0.0004021421],"domain_scores_gemma":[0.9978826,0.0002155601,0.001241901,0.0002101409,0.000282568,0.0001672274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001104114,0.0008247179,0.2038638,0.0002356471,0.0002914584,0.00003086785,0.002500053,0.4661324,0.000202312,0.2664506,0.01753596,0.04082799],"study_design_scores_gemma":[0.003233196,0.001139728,0.6546608,0.00006169271,0.00005293783,0.0000193069,0.00005234413,0.2360337,0.00007324842,0.08364501,0.02035347,0.0006745955],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6707467,0.0004655169,0.3265027,0.0005670738,0.000642273,0.0002575268,0.000591923,0.00002841476,0.0001978228],"genre_scores_gemma":[0.9925613,0.000225505,0.006143507,0.00005077775,0.0005466685,0.00002442748,0.00004395416,0.00004213018,0.0003617232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.450797,"threshold_uncertainty_score":0.8451359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06773781870515753,"score_gpt":0.2387599105451792,"score_spread":0.1710220918400217,"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."}}