{"id":"W2097047785","doi":"10.1111/j.1467-9892.2007.00534.x","title":"Using Difference‐Based Methods for Inference in Regression with Fractionally Integrated Processes","year":2007,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Inference; Autoregressive model; Delta method; Kernel (algebra); Kernel regression; Sample size determination; Regression analysis; Regression; Statistical inference; Applied mathematics; Econometrics; Statistics; Algorithm; Mathematical optimization; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001528488,0.0001464242,0.0007174833,0.001145431,0.0000723486,0.00006431921,0.0001654735,0.00008314144,0.0003815511],"category_scores_gemma":[0.0006133016,0.0001149842,0.0002026215,0.0009695585,0.00004469256,0.0004809586,0.00001111204,0.0001655873,0.000003313459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001510871,"about_ca_system_score_gemma":0.0001114327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003680322,"about_ca_topic_score_gemma":0.0002175865,"domain_scores_codex":[0.9986154,0.00002547104,0.0009232016,0.0001713475,0.00003422263,0.0002303295],"domain_scores_gemma":[0.9980093,0.0004298558,0.001226563,0.0001367258,0.0001098921,0.00008772037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003081191,0.0003684797,0.6275022,0.0001685673,0.002932397,0.00002108081,0.0007861632,0.3560956,0.0007870374,0.0008757114,0.0001041232,0.007277453],"study_design_scores_gemma":[0.001912161,0.0008966677,0.09662692,0.0002254979,0.0006637812,0.00003109768,0.0004177654,0.8807706,0.00301248,0.00946216,0.005316186,0.0006647418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3567675,0.0003370848,0.6423636,0.0002115657,0.00004009261,0.00006139337,0.00002987513,0.000004044028,0.0001847881],"genre_scores_gemma":[0.8050003,0.00006856504,0.1943269,0.0001001197,0.00006768263,0.000001671873,0.00001411273,0.00001206579,0.0004085308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5308753,"threshold_uncertainty_score":0.4688921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1076987995831394,"score_gpt":0.3555961054527315,"score_spread":0.2478973058695921,"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."}}