{"id":"W4412817163","doi":"10.1002/jae.70004","title":"Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada; Carleton University","funders":"","keywords":"Dynamic stochastic general equilibrium; Inference; Identification (biology); Econometrics; Nonlinear system; Computer science; Sample (material); Economics; Artificial intelligence; Macroeconomics; Monetary policy; Physics","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":[0.0116744,0.0008987174,0.001513082,0.001818188,0.0004558868,0.00185965,0.002015291,0.001135251,0.003517275],"category_scores_gemma":[0.08035659,0.001013784,0.001571619,0.0009786353,0.002470728,0.002319813,0.003242175,0.002542777,0.0004295515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085283,"about_ca_system_score_gemma":0.001771905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002989764,"about_ca_topic_score_gemma":0.001892331,"domain_scores_codex":[0.9960119,0.002686972,0.0001513243,0.0004125067,0.0006029562,0.0001345342],"domain_scores_gemma":[0.9348598,0.05829149,0.002292091,0.002422023,0.001753384,0.0003813076],"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":[0.00004761426,0.00002947844,0.000931993,0.00006618042,0.00009907295,0.00005665993,0.00007108062,0.8410066,0.000497855,0.1432936,0.0003437574,0.0135561],"study_design_scores_gemma":[0.000007473632,0.000008791976,0.00009672821,0.0000111981,0.00000565528,0.000007307625,0.00000451225,0.9464716,0.0002446259,0.05293493,0.0001995029,0.000007632732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006402976,0.0000621365,0.9926838,0.0001238841,0.000008859894,0.00001593841,0.0000399473,0.0001114824,0.0005508276],"genre_scores_gemma":[0.7226191,0.0004576351,0.2736006,0.000195071,0.00008170256,0.0003664423,0.0004952291,0.0002041132,0.00198015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0116744,"threshold_uncertainty_score":0.06174088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1951532185892277,"score_gpt":0.2686493352945823,"score_spread":0.07349611670535464,"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."}}