{"id":"W3020932207","doi":"10.1080/07474938.2017.1307548","title":"Identication-robust moment-based tests for Markov switching in autoregressive models","year":2020,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"Université de Montréal; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; McGill University","keywords":"Autoregressive model; SETAR; STAR model; Markov chain Monte Carlo; Context (archaeology); Econometrics; Markov chain; Computer science; Inference; Monte Carlo method; Mathematics; Statistics; Autoregressive integrated moving average; Artificial intelligence; Time series","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02928199,0.0009022893,0.00265015,0.004036605,0.001056496,0.002774314,0.002903916,0.002186631,0.007528638],"category_scores_gemma":[0.2160634,0.0006985894,0.002210455,0.002564136,0.003520584,0.00453397,0.003371449,0.003622033,0.0009627067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314895,"about_ca_system_score_gemma":0.002915578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00181159,"about_ca_topic_score_gemma":0.0009408224,"domain_scores_codex":[0.9636828,0.02538757,0.00175825,0.003951485,0.004018439,0.001201523],"domain_scores_gemma":[0.6311523,0.3412165,0.01368897,0.009293525,0.003543673,0.001105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001446818,0.000539934,0.06471405,0.0005929192,0.001916751,0.000960968,0.001108428,0.1954696,0.004040794,0.5051095,0.005127404,0.218973],"study_design_scores_gemma":[0.0002582144,0.0004803229,0.01460663,0.0001278475,0.0002146629,0.0002575277,0.0003368221,0.6486377,0.003281556,0.3291041,0.00253855,0.0001560908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09644275,0.0006272928,0.8966318,0.0007724006,0.00009834339,0.000162164,0.0005042625,0.00088624,0.003874738],"genre_scores_gemma":[0.9221004,0.000247071,0.07492634,0.0002146799,0.0002020456,0.0003458544,0.0008727892,0.0001300456,0.0009608211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02928199,"threshold_uncertainty_score":0.1548598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05684072058827832,"score_gpt":0.2057321905978461,"score_spread":0.1488914700095678,"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."}}