{"id":"W2407006977","doi":"10.1111/caje.12149","title":"Identification of technology shocks using misspecified VARs","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Identification (biology); Lag; Range (aeronautics); Shock (circulatory); Technology shock; Process (computing); Per capita; Computer science; Economics; Macroeconomics; Monetary policy; Engineering; Dynamic stochastic general equilibrium","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":[0.00629682,0.001016102,0.001171647,0.002328436,0.0002837026,0.0022668,0.00100433,0.001137139,0.002740424],"category_scores_gemma":[0.03689448,0.0006412486,0.001240675,0.001571274,0.0006196933,0.001995052,0.001408022,0.002222378,0.0005167144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005560886,"about_ca_system_score_gemma":0.0009352855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002748009,"about_ca_topic_score_gemma":0.002158476,"domain_scores_codex":[0.9972981,0.001441746,0.0002482155,0.0004358256,0.0004123744,0.0001637451],"domain_scores_gemma":[0.9633471,0.02912738,0.003956084,0.002154479,0.001224795,0.000190182],"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":[0.0002087292,0.0001234573,0.0329374,0.0001862058,0.000410044,0.0002490675,0.0001561328,0.8768771,0.001612814,0.03144468,0.0009863483,0.05480806],"study_design_scores_gemma":[0.00002054715,0.00006723057,0.008675775,0.00004296778,0.00003673014,0.0000377791,0.00004262197,0.9759089,0.00114989,0.01324524,0.0007455339,0.00002667696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2382152,0.0003765222,0.7570687,0.0003576318,0.0001042319,0.0001023677,0.0007560307,0.0005732653,0.002446098],"genre_scores_gemma":[0.9498351,0.0003783371,0.0468987,0.00008055373,0.00008398227,0.0000938513,0.001239952,0.00005767735,0.001331814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00629682,"threshold_uncertainty_score":0.03330112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3116300811573207,"score_gpt":0.2083489852573668,"score_spread":0.1032810958999539,"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."}}