{"id":"W3188629815","doi":"10.1093/ectj/utab028","title":"Detecting common breaks in the means of high dimensional cross-dependent panels","year":2021,"lang":"en","type":"article","venue":"Econometrics Journal","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"CUSUM; Statistic; Monte Carlo method; Mathematics; Series (stratigraphy); Test statistic; Applied mathematics; Statistics; Panel data; Asymptotic distribution; Statistical hypothesis testing; Estimator","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003122116,0.0001987967,0.0006562624,0.001000947,0.0002262733,0.0002829321,0.0005095031,0.0001392554,0.001916725],"category_scores_gemma":[0.0005747264,0.0001943748,0.0002599264,0.0008143334,0.00007496349,0.0005082202,0.000104687,0.0006658981,0.0002161022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002193835,"about_ca_system_score_gemma":0.0000522441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006047059,"about_ca_topic_score_gemma":0.0001019992,"domain_scores_codex":[0.9974079,0.00008280321,0.001586054,0.0003525237,0.00007536528,0.0004953824],"domain_scores_gemma":[0.9979655,0.000486824,0.0009371316,0.0004354565,0.00003840715,0.0001367378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000709849,0.0004982459,0.9001339,0.00005136578,0.0002830658,0.0002153497,0.002033057,0.05942204,0.00004033566,0.02495615,0.0005083157,0.01178716],"study_design_scores_gemma":[0.002847592,0.0001986186,0.8795313,0.00003753417,0.00001981113,0.001740367,0.0004444855,0.0130575,0.001056776,0.09640793,0.004060226,0.0005978834],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879237,0.002503917,0.000710525,0.0007597454,0.0008789047,0.00009131295,0.0001860953,0.000007009561,0.006938741],"genre_scores_gemma":[0.9979538,0.0003236612,0.0004763108,0.0005965172,0.0003014665,0.00000428849,0.00001207294,0.00002306132,0.0003088168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07145178,"threshold_uncertainty_score":0.9989957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013008164762433,"score_gpt":0.2606019260068582,"score_spread":0.1593011095306149,"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."}}