{"id":"W2153013713","doi":"10.1081/etc-200028211","title":"Semiparametric Efficient Estimation of the Mean of a Time Series in the Presence of Conditional Heterogeneity of Unknown Form","year":2005,"lang":"en","type":"article","venue":"Econometric Reviews","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Science Foundation","keywords":"Estimator; Mathematics; Kernel density estimation; Series (stratigraphy); Nonparametric statistics; Ergodic theory; Applied mathematics; Conditional expectation; Upper and lower bounds; Kernel (algebra); Conditional probability distribution; Statistics; Econometrics; Combinatorics; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002586314,0.000150793,0.0009223624,0.0008311279,0.00003270369,0.000009080391,0.0005609693,0.00006840994,0.0004143023],"category_scores_gemma":[0.0008742437,0.0001109767,0.0003411097,0.001649565,0.0001855135,0.0002515749,0.00006852167,0.0001039797,0.00007469282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007886806,"about_ca_system_score_gemma":0.00002294208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001952557,"about_ca_topic_score_gemma":0.00003011054,"domain_scores_codex":[0.9973593,0.00008396283,0.002074672,0.0002295048,0.00005634968,0.0001961705],"domain_scores_gemma":[0.9968156,0.0003721507,0.002145918,0.0006124513,0.00002076969,0.00003307282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001088864,0.001669914,0.1830785,0.002150161,0.0003200326,3.474415e-7,0.005360404,0.6837322,0.00007951567,0.08702308,0.002983515,0.0334935],"study_design_scores_gemma":[0.002045393,0.0006207066,0.610615,0.0003733404,0.00009175158,0.00002905714,0.0001103214,0.3188134,0.008181246,0.015901,0.04250536,0.0007133835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982038,0.01226803,0.000350502,0.0002022991,0.00007383504,0.0007186112,0.0003454552,0.000002252883,0.004001022],"genre_scores_gemma":[0.9971823,0.00177992,0.0007991898,0.00005437919,0.00002636228,0.0000275639,0.00001644498,0.000007708485,0.0001061477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4275365,"threshold_uncertainty_score":0.453632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06729178080974686,"score_gpt":0.2597942150857737,"score_spread":0.1925024342760268,"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."}}