{"id":"W3124480753","doi":"","title":"Seasonal and Periodic Long Memory Models in the Inflation Rates","year":2005,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive fractionally integrated moving average; Econometrics; Outlier; Inflation (cosmology); Seasonality; Parametric statistics; Statistics; Long memory; Maximum likelihood; Mathematics; Series (stratigraphy); Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004673465,0.0002991588,0.0006471532,0.0006810609,0.0002061276,0.000328327,0.0006134068,0.0004903579,0.00005610557],"category_scores_gemma":[0.0003767357,0.0003253531,0.0001400441,0.0001792675,0.0002524345,0.0003516145,0.000571137,0.001874075,0.00001880473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006300744,"about_ca_system_score_gemma":0.0002465511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005486897,"about_ca_topic_score_gemma":0.002179153,"domain_scores_codex":[0.9970124,0.0001476968,0.001075653,0.0009934176,0.00009842248,0.0006723676],"domain_scores_gemma":[0.9984544,0.0003857642,0.0002789022,0.0007345263,0.00005175141,0.00009464153],"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":[0.0001866086,0.0002915382,0.308176,0.0003511218,0.00006423881,0.00002995707,0.01013334,0.3875095,0.000004056237,0.02919408,0.00005953501,0.2640001],"study_design_scores_gemma":[0.0005215813,0.00002751199,0.1597855,0.0001138207,0.000002563809,0.000004272989,0.0004863753,0.7778898,0.000002673607,0.05853669,0.002250387,0.0003787361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609752,0.003479292,0.00008784488,0.0009818155,0.0002221801,0.0007598243,0.000102387,0.00001687407,0.03337461],"genre_scores_gemma":[0.9806829,0.01772136,0.0004315235,0.000120073,0.0003415877,0.000188527,0.0000582204,0.00004192788,0.0004138665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3903804,"threshold_uncertainty_score":0.9999198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06385595174849884,"score_gpt":0.3023878766300434,"score_spread":0.2385319248815445,"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."}}