{"id":"W2613772044","doi":"10.2866/773816","title":"How to predict financial stress? An assessment of Markov switching models","year":2017,"lang":"en","type":"article","venue":"Econstor (Econstor)","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Humanities; Markov chain; Economics; Econometrics; Mathematics; Philosophy; Statistics","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":[0.007952762,0.001163614,0.001383063,0.001145755,0.0004118748,0.001672524,0.00116157,0.001457891,0.002181072],"category_scores_gemma":[0.01761681,0.0005957803,0.001240795,0.0006697123,0.0006044812,0.001876029,0.0008805651,0.001641111,0.0003054089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008695762,"about_ca_system_score_gemma":0.001001896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01308841,"about_ca_topic_score_gemma":0.006471229,"domain_scores_codex":[0.9988217,0.0007442774,0.00005548452,0.0001697035,0.0000911762,0.0001175952],"domain_scores_gemma":[0.9802421,0.01770308,0.0008444812,0.0003219283,0.0005369807,0.0003514141],"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":[0.0002113884,0.00008307183,0.01864832,0.000086004,0.0001787087,0.00006846636,0.0001204791,0.942996,0.0002089161,0.01757373,0.0008145126,0.0190103],"study_design_scores_gemma":[0.000008009322,0.00003315795,0.0009763748,0.00001711028,0.00001609893,0.000008725876,0.00001600818,0.9887616,0.00003862022,0.009997586,0.0001180546,0.000008683994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5276072,0.00333621,0.455452,0.004841914,0.0002248941,0.0002212238,0.001204105,0.0006385962,0.006473894],"genre_scores_gemma":[0.9769446,0.00127444,0.01874177,0.0002120276,0.0001696387,0.0001191872,0.0007449718,0.00002865145,0.001764723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01308841,"threshold_uncertainty_score":0.04205877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02459553528341038,"score_gpt":0.2619009041777907,"score_spread":0.2373053688943803,"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."}}