{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02988212,0.0008683504,0.002255629,0.002746143,0.0007434393,0.001774454,0.003450368,0.002033816,0.003230185],"category_scores_gemma":[0.1065165,0.0009163836,0.002171404,0.001886149,0.003465213,0.003053675,0.002582009,0.004260413,0.0004977735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001841152,"about_ca_system_score_gemma":0.001694775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003581056,"about_ca_topic_score_gemma":0.002044394,"domain_scores_codex":[0.9875464,0.008833906,0.0005262818,0.001329353,0.001473323,0.0002907566],"domain_scores_gemma":[0.8894088,0.1009955,0.002045571,0.00427543,0.002876109,0.0003984375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003753676,0.0002249151,0.004717636,0.0004151099,0.0005714413,0.0002931866,0.0004633542,0.2857438,0.003606073,0.5159647,0.001383244,0.1862412],"study_design_scores_gemma":[0.00005202196,0.00005059409,0.0003241249,0.00002589931,0.00002919723,0.00003248467,0.00001508901,0.8768596,0.0008357,0.1208214,0.0009330325,0.00002077724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001722669,0.00006180321,0.9978459,0.0000664097,0.00002657069,0.00001628305,0.00001147998,0.0000637729,0.0001850269],"genre_scores_gemma":[0.1240277,0.0002093007,0.8739319,0.0002110713,0.0001233508,0.0002706867,0.0001294362,0.0001246642,0.0009719109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02988212,"threshold_uncertainty_score":0.1580336,"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."}}