{"id":"W2060577210","doi":"10.1111/1467-9892.00308","title":"First‐Order Autoregressive Processes with Heterogeneous Persistence","year":2003,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; STAR model; Nonlinear autoregressive exogenous model; Mathematics; SETAR; Estimator; Applied mathematics; Econometrics; Autoregressive–moving-average model; Statistics; Autoregressive integrated moving average; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003344883,0.0001427475,0.0006219152,0.0003672784,0.0001611295,0.00008287585,0.0001815761,0.00006247085,0.0006688885],"category_scores_gemma":[0.0004497026,0.0001211662,0.0002762843,0.000975524,0.00006453783,0.0004204453,0.00001406758,0.0001230895,0.00004177486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006462415,"about_ca_system_score_gemma":0.0001041721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000441778,"about_ca_topic_score_gemma":0.0002216136,"domain_scores_codex":[0.9988679,0.00001440172,0.0006342886,0.0002020488,0.00007980024,0.0002015542],"domain_scores_gemma":[0.9984982,0.00004755235,0.0008587192,0.0001987434,0.0003126828,0.00008408903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004983509,0.000396249,0.4712123,0.0002783716,0.006444672,0.0001765951,0.004564783,0.5098529,0.00001568237,0.005859315,0.0004218301,0.0002789203],"study_design_scores_gemma":[0.008616433,0.009685565,0.07855131,0.001243266,0.0124865,0.002220933,0.005218417,0.3765659,0.004132529,0.08145151,0.4126371,0.007190492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8713384,0.0159388,0.1058309,0.001178901,0.0001763963,0.0001451503,0.00006956071,0.00002727109,0.005294593],"genre_scores_gemma":[0.987181,0.0008242525,0.01036221,0.00004513521,0.00005032345,0.000001963382,0.000001999995,0.00001498338,0.001518121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4122153,"threshold_uncertainty_score":0.732386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762777842930505,"score_gpt":0.19533800698509,"score_spread":0.1777102285557849,"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."}}