{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002126973,0.0004219349,0.0005389066,0.0007367402,0.0003008667,0.001151554,0.0009316222,0.001314114,0.002248466],"category_scores_gemma":[0.01162219,0.0002877355,0.001042815,0.001031684,0.0005586256,0.001845081,0.0006872666,0.001036849,0.0003737939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005448132,"about_ca_system_score_gemma":0.0005243589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00413159,"about_ca_topic_score_gemma":0.00353906,"domain_scores_codex":[0.9993529,0.0002357791,0.00004143724,0.0001257563,0.0001368827,0.0001072642],"domain_scores_gemma":[0.9977754,0.001196365,0.0005838285,0.0002040227,0.0001764279,0.00006408425],"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":[0.0001485756,0.0001754838,0.0510528,0.0002389178,0.0002840817,0.0009276579,0.000936497,0.508168,0.003403275,0.3509499,0.002594846,0.08111998],"study_design_scores_gemma":[0.00002565786,0.0001023831,0.01877503,0.00005960853,0.00007588886,0.0004056698,0.0001654659,0.8205326,0.0006028746,0.1556509,0.003555753,0.00004819431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.607673,0.003158633,0.3707882,0.002080529,0.000243996,0.00008542195,0.0004757686,0.0003448566,0.01514951],"genre_scores_gemma":[0.9810494,0.001220194,0.01321381,0.0000999265,0.0001476546,0.00005393268,0.0002458453,0.0000354415,0.003933701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00413159,"threshold_uncertainty_score":0.01124865,"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."}}