{"id":"W2127831890","doi":"10.1214/009053605000000309","title":"Exact local Whittle estimation of fractional integration","year":2005,"lang":"en","type":"article","venue":"The Annals of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Economic and Social Research Council; National Science Foundation","keywords":"Tapering; Estimator; Mathematics; Applied mathematics; Limit (mathematics); Distribution (mathematics); Estimation; Long memory; Mathematical optimization; Econometrics; Statistics; Mathematical analysis; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.005523392,0.0006936412,0.001417123,0.001299354,0.0004927964,0.002029801,0.001604287,0.001269077,0.003878769],"category_scores_gemma":[0.02977535,0.0005616653,0.0008229845,0.0009581202,0.002278515,0.004739721,0.002454021,0.001765987,0.0005628904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016728,"about_ca_system_score_gemma":0.0009631669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001986662,"about_ca_topic_score_gemma":0.002089975,"domain_scores_codex":[0.9983978,0.0007449242,0.00006586606,0.0003555446,0.0002671402,0.000168793],"domain_scores_gemma":[0.9906287,0.005864624,0.0008055235,0.001662752,0.0007661611,0.000272197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002502209,0.00006564293,0.00422529,0.0001621836,0.0001070663,0.0002198967,0.0002666441,0.547287,0.004073685,0.3617865,0.0009039234,0.08065186],"study_design_scores_gemma":[0.000007917665,0.00002392002,0.000407399,0.00001370716,0.000007929816,0.00003619194,0.00001538084,0.9207232,0.00110777,0.07729538,0.0003396915,0.00002155794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0229967,0.0001473508,0.9757814,0.00007234908,0.00001379013,0.00001207572,0.00003229061,0.0002353819,0.0007086536],"genre_scores_gemma":[0.7563686,0.0003712049,0.2377409,0.0001537014,0.00008593841,0.000111787,0.0003041605,0.0002265646,0.004637118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005523392,"threshold_uncertainty_score":0.02921087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09679765184038333,"score_gpt":0.3121505688133448,"score_spread":0.2153529169729615,"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."}}