{"id":"W3123630768","doi":"10.22004/ag.econ.273611","title":"Wild Bootstrap Tests for IV Regression","year":2008,"lang":"en","type":"preprint","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Heteroscedasticity; Econometrics; Confidence interval; Regression; Statistics; Linear regression; Mathematics; Regression analysis; Computer science","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":[0.02639939,0.001100717,0.001838558,0.003994934,0.001215205,0.002170608,0.003447378,0.002228979,0.01340645],"category_scores_gemma":[0.2122558,0.0005918477,0.001592534,0.004214469,0.003463659,0.00439858,0.002977677,0.00405316,0.002919261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007613156,"about_ca_system_score_gemma":0.001408746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008447007,"about_ca_topic_score_gemma":0.0005751377,"domain_scores_codex":[0.9656391,0.02780346,0.0009443472,0.002063398,0.003004983,0.0005446107],"domain_scores_gemma":[0.8282694,0.1414927,0.006289505,0.01685596,0.005886859,0.001205624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003253625,0.0002210697,0.01491036,0.0003264152,0.0005386508,0.0004498747,0.0003435175,0.04791394,0.001329937,0.6612747,0.01469939,0.2576668],"study_design_scores_gemma":[0.0001180235,0.0002243257,0.002717682,0.0001643883,0.00007701207,0.0002741753,0.000145629,0.2892051,0.001657964,0.69271,0.01264172,0.00006388192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007145193,0.0003629525,0.9890181,0.0003086815,0.0001285257,0.00008672817,0.0002737663,0.000478289,0.00219784],"genre_scores_gemma":[0.3476832,0.0006170638,0.6430609,0.0008334211,0.0005848135,0.001383421,0.001899048,0.0008139635,0.00312417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02639939,"threshold_uncertainty_score":0.139615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2696328283530562,"score_gpt":0.4046982912113298,"score_spread":0.1350654628582735,"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."}}