{"id":"W3037592550","doi":"10.1002/for.2716","title":"Forecast performance and bubble analysis in noncausal MAR(1, 1) processes","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Futures contract; Econometrics; Estimator; Nonlinear system; Bubble; Term (time); Gaussian; Variance (accounting); Mathematics; Series (stratigraphy); Lévy process; Applied mathematics; Economics; Statistical physics; Computer science; Statistics; Financial economics; Physics; Geology","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":[0.0061025,0.0006374284,0.0007025747,0.0006794633,0.0002087467,0.001105919,0.0006300909,0.0009413357,0.0009521518],"category_scores_gemma":[0.01970885,0.0003228108,0.0004239698,0.000325868,0.0007501206,0.001313014,0.0006482021,0.001190492,0.0001431628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005672397,"about_ca_system_score_gemma":0.0006696606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01095404,"about_ca_topic_score_gemma":0.003522632,"domain_scores_codex":[0.9992919,0.0004006143,0.00003130116,0.00009247695,0.0001301402,0.00005362522],"domain_scores_gemma":[0.9839355,0.01387727,0.0006860893,0.0004056638,0.0009380752,0.0001574429],"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":[0.0002414462,0.00003229519,0.003733438,0.00003993565,0.00004141011,0.00005052143,0.00004484,0.9767525,0.00107779,0.004968275,0.0001835217,0.012834],"study_design_scores_gemma":[0.000002365373,0.000009961091,0.0002658117,0.000001224469,0.000001720263,0.000002837465,0.000001774359,0.9990733,0.0001811064,0.0004464845,0.00001121925,0.000002216889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.692269,0.0006585146,0.3039647,0.0005030927,0.00005397636,0.00003544318,0.0001505868,0.0004899224,0.00187484],"genre_scores_gemma":[0.991693,0.0001161758,0.007737005,0.00001773388,0.00001516931,0.000007092822,0.0001352659,0.00001488996,0.0002636038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01095404,"threshold_uncertainty_score":0.03227353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588153369421907,"score_gpt":0.2193155033465456,"score_spread":0.1634339696523266,"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."}}