{"id":"W4382874219","doi":"10.3982/qe1626","title":"Bootstrap inference under cross‐sectional dependence","year":2023,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Center for Interuniversity Research and Analysis on Organizations; Western University","funders":"Swenson College of Science and Engineering, University of Minnesota Duluth; Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; Chinese University of Hong Kong","keywords":"Inference; Econometrics; Studentized range; Statistics; Mathematics; Representation (politics); Kernel (algebra); Spatial analysis; Statistical inference; Regression; Computer science; Standard error; Artificial intelligence; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008145517,0.0002540213,0.0004283972,0.0004312513,0.000248334,0.0002405782,0.0003869052,0.0001671163,0.001611406],"category_scores_gemma":[0.0002009395,0.0003316963,0.0001967098,0.0002246139,0.000197775,0.0008886162,0.0000996302,0.0002363552,0.02194268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001961888,"about_ca_system_score_gemma":0.00004465157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004206742,"about_ca_topic_score_gemma":0.00009649759,"domain_scores_codex":[0.9977899,0.0000205381,0.0008909762,0.0006835226,0.00002327884,0.0005917847],"domain_scores_gemma":[0.9986517,0.0003508137,0.0004173086,0.0003936728,0.00001949035,0.0001670307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002734503,0.00002426095,0.2912615,0.00001129401,0.00007144267,0.00000225106,0.0002442748,0.08667906,0.00001094735,0.6209269,0.0006796994,0.00006096523],"study_design_scores_gemma":[0.0004750865,0.00009348748,0.621664,0.00000481848,0.000002548051,0.000006532586,0.0001217834,0.1022752,0.00005506118,0.2680493,0.006821926,0.0004303061],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745042,0.0002126777,0.004453205,0.0004635284,0.001033822,0.0001698702,0.000614899,0.0001535882,0.01839422],"genre_scores_gemma":[0.9947084,0.0004515029,0.0008834567,0.0005369112,0.000160375,0.00003123768,0.0000986278,0.00004159859,0.003087883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3528777,"threshold_uncertainty_score":0.9999135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3198245107243074,"score_gpt":0.3557129964063339,"score_spread":0.03588848568202652,"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."}}