{"id":"W2013515152","doi":"10.1016/s0304-4076(00)00086-5","title":"Nested random effects estimation in unbalanced panel data","year":2001,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Panel data; Econometrics; Random effects model; Nested set model; Generalization; Statistics; Monte Carlo method; Mathematics; Computer science; Data mining","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":[0.001747002,0.000142904,0.0007603237,0.002233194,0.00004146797,0.0001079944,0.0007244764,0.000100133,0.0002853314],"category_scores_gemma":[0.002145681,0.0001474314,0.0001277342,0.002343914,0.00002494144,0.001094638,0.00009771857,0.0002275546,0.0001993804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279862,"about_ca_system_score_gemma":0.00003147574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001905565,"about_ca_topic_score_gemma":0.00006061236,"domain_scores_codex":[0.9980518,0.00003192676,0.001339796,0.0002790564,0.00005874899,0.0002387132],"domain_scores_gemma":[0.997669,0.0004071675,0.001212782,0.0005362852,0.00005718143,0.0001175796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002380142,0.0003780026,0.9442538,0.0000727622,0.0002661255,0.0001666138,0.0002012779,0.009807023,0.00001236298,0.003012677,0.001845417,0.03974595],"study_design_scores_gemma":[0.009432848,0.0003413399,0.6437148,0.00009104369,0.00009978862,0.0001666456,0.00009972618,0.2850302,0.00002946203,0.01974767,0.04061806,0.0006283772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8798008,0.007594537,0.1047906,0.0009216997,0.001375362,0.0002503603,0.0002357833,0.00001720365,0.005013702],"genre_scores_gemma":[0.9951239,0.001934956,0.002340797,0.0001702051,0.0001682958,0.00000233175,0.0001126793,0.00001558988,0.0001313012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.300539,"threshold_uncertainty_score":0.6012079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2030697619813059,"score_gpt":0.260638757775089,"score_spread":0.05756899579378311,"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."}}