{"id":"W2742631142","doi":"","title":"Efficiency in Large Dynamic Panel Models with Common Factor","year":2008,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation du Risque; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Unobservable; Estimator; Dynamic factor; Macro; Econometrics; Identification (biology); Factor analysis; Nonlinear system; Panel data; Mathematics; Applied mathematics; Specification; Computer science; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008469904,0.0002057213,0.0006125869,0.0008834794,0.0001863927,0.00005246253,0.0005322291,0.0001549412,0.0003061738],"category_scores_gemma":[0.00008604053,0.0002172999,0.000102006,0.0005420541,0.0001864104,0.0003589274,0.0001588552,0.0005541179,0.0001181398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004820457,"about_ca_system_score_gemma":0.00009344338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001319555,"about_ca_topic_score_gemma":0.006036526,"domain_scores_codex":[0.9975685,0.00005982419,0.0007408198,0.0007186212,0.00008046655,0.0008317904],"domain_scores_gemma":[0.9987835,0.0001881144,0.000164387,0.000690024,0.00002890949,0.0001450522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001896078,0.0008067663,0.9340974,0.00004947877,0.00009380504,0.0001900412,0.002027108,0.03103048,0.00002354657,0.01292787,0.00002209488,0.01854177],"study_design_scores_gemma":[0.002476222,0.0002743889,0.3399473,0.0000514725,0.000003571918,0.0000325147,0.0006685659,0.6425199,0.00002425433,0.006074277,0.007151826,0.00077571],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9471883,0.0002560779,0.0001790009,0.0001437191,0.0000624015,0.0002761869,0.0003962839,0.00002196918,0.05147607],"genre_scores_gemma":[0.9936932,0.004753002,0.0002102315,0.0000754524,0.00002731631,0.00005518831,0.00008949875,0.00003671282,0.001059366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6114894,"threshold_uncertainty_score":0.8861234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07314110909673183,"score_gpt":0.27855957922967,"score_spread":0.2054184701329382,"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."}}