{"id":"W3194869296","doi":"10.3390/jrfm14090405","title":"FDML versus GMM for Dynamic Panel Models with Roots Near Unity","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Generalized method of moments; Panel data; Econometrics; Mixture model; Maximum likelihood; Norm (philosophy); Computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01248221,0.0008116413,0.001050795,0.001725093,0.0004022305,0.001840242,0.001030318,0.001892211,0.003095984],"category_scores_gemma":[0.06786171,0.0004975155,0.0008441818,0.001924838,0.0008010578,0.002875954,0.001520623,0.001527961,0.000824675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326098,"about_ca_system_score_gemma":0.001294851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01497463,"about_ca_topic_score_gemma":0.009285433,"domain_scores_codex":[0.9920981,0.006402546,0.0001885341,0.0006374907,0.0004925168,0.0001808669],"domain_scores_gemma":[0.9625063,0.03370559,0.0009811197,0.001748004,0.0008546053,0.0002044208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009613324,0.00009204184,0.01789886,0.000855916,0.0008512856,0.0002506856,0.0005331477,0.5540476,0.0009778327,0.1049023,0.01347841,0.3051507],"study_design_scores_gemma":[0.0001040329,0.0001972317,0.00761815,0.0001669621,0.0001847974,0.0001919494,0.0003602352,0.9170239,0.0009940552,0.05625937,0.01681858,0.00008073049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1192979,0.01375375,0.8464381,0.00348741,0.000366157,0.0002214435,0.00259666,0.003946759,0.009891908],"genre_scores_gemma":[0.6821372,0.00447805,0.3043487,0.000739857,0.0003195225,0.0003182633,0.00387604,0.0007617337,0.003020608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01497463,"threshold_uncertainty_score":0.06601304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05903549155349872,"score_gpt":0.2217475037676825,"score_spread":0.1627120122141837,"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."}}