{"id":"W2735164070","doi":"10.1016/j.jeconom.2018.03.006","title":"The asymptotic properties of GMM and indirect inference under second-order identification","year":2018,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Estimator; Mathematics; Inference; Indirect Inference; Asymptotic analysis; Moment (physics); Applied mathematics; Neighbourhood (mathematics); Identification (biology); Context (archaeology); Econometrics; Asymptotic distribution; Limit (mathematics); Mathematical optimization; Statistics; Computer science; Mathematical analysis; Artificial intelligence","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":[0.001744518,0.0001286263,0.0004402668,0.0008190805,0.0001638846,0.0001565481,0.0003260541,0.00008562706,0.0002820954],"category_scores_gemma":[0.000901154,0.0001050284,0.0001008762,0.0005110991,0.0002589554,0.0006075316,0.00005651982,0.0001708495,0.0001045084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009021338,"about_ca_system_score_gemma":0.00004710736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003871166,"about_ca_topic_score_gemma":0.00003137044,"domain_scores_codex":[0.9981833,0.00002402668,0.001348898,0.000172982,0.0000362511,0.0002345816],"domain_scores_gemma":[0.997585,0.0002279348,0.001713261,0.0002650849,0.0001102014,0.0000985235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005400063,0.0007123524,0.7445228,0.0005215291,0.002696768,0.000005230695,0.008311233,0.003163231,0.001531514,0.1878824,0.008648647,0.04146424],"study_design_scores_gemma":[0.00194597,0.001173605,0.8460056,0.00009174024,0.00006955953,0.0001176398,0.0008512185,0.01241977,0.006199207,0.08567228,0.04472381,0.0007295785],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882631,0.006085553,0.001089228,0.0005984872,0.0006859829,0.00009678931,0.00002727243,0.000004279216,0.003149293],"genre_scores_gemma":[0.9973747,0.001278886,0.0002074395,0.0001506699,0.0002296627,0.000001853519,7.707768e-7,0.00001391304,0.0007421361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1022102,"threshold_uncertainty_score":0.4282933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2944871838493995,"score_gpt":0.2457262875397585,"score_spread":0.04876089630964101,"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."}}