{"id":"W3183569533","doi":"10.24149/wp2019","title":"Impulse Response Analysis for Structural Dynamic Models with Nonlinear Regressors","year":2020,"lang":"en","type":"article","venue":"Federal Reserve Bank of Dallas, Working Papers","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Estimator; Autoregressive model; Impulse response; Nonlinear system; Population; Impulse (physics); Applied mathematics; Mathematics; Linear model; Monte Carlo method; Population model; Econometrics; Statistics; Physics; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004719059,0.0006896349,0.0008993933,0.001252554,0.0002816519,0.001126526,0.0009733179,0.001268749,0.005319938],"category_scores_gemma":[0.02572514,0.0005348858,0.001210955,0.001136744,0.001346286,0.001495818,0.00147212,0.001979613,0.0007398676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008750853,"about_ca_system_score_gemma":0.0006864664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002139184,"about_ca_topic_score_gemma":0.001332873,"domain_scores_codex":[0.9980074,0.001301882,0.0000509563,0.0002024824,0.0002599955,0.0001774226],"domain_scores_gemma":[0.9816415,0.0160222,0.001108704,0.0004989302,0.0005748775,0.0001537136],"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":[0.00005827857,0.0000825539,0.003624584,0.0001441963,0.0001586607,0.0001764869,0.0003293236,0.6045674,0.001068024,0.365033,0.00127922,0.02347821],"study_design_scores_gemma":[0.000007176094,0.00002173212,0.0004371753,0.00001438436,0.0000128335,0.00002056789,0.00003195475,0.9001901,0.0001471164,0.09853952,0.0005655708,0.00001195846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03056916,0.0002475843,0.9651114,0.0005749526,0.00003457308,0.00002695576,0.00007738175,0.0001245094,0.003233473],"genre_scores_gemma":[0.8855831,0.00144162,0.09628092,0.0003328186,0.000176196,0.0003684384,0.0004610419,0.0001765021,0.01517934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005319938,"threshold_uncertainty_score":0.02495712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04188805417643269,"score_gpt":0.2485717098352929,"score_spread":0.2066836556588602,"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."}}