{"id":"W2549670306","doi":"10.1111/caje.12218","title":"News shocks and labour market dynamics in matching models","year":2016,"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":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leverhulme Trust","keywords":"Economics; Business cycle; Unemployment; Investment (military); Matching (statistics); Econometrics; Productivity; Total factor productivity; Dynamic stochastic general equilibrium; Bayesian probability; Monetary economics; Macroeconomic model; Baseline (sea); Macroeconomics; Monetary policy","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":[0.002567968,0.0004238571,0.001390479,0.001126756,0.0006293707,0.002363472,0.001195543,0.002103498,0.01007724],"category_scores_gemma":[0.01601829,0.000485529,0.0007070029,0.001356824,0.001046359,0.00274873,0.001364291,0.001222149,0.00110937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294401,"about_ca_system_score_gemma":0.0007072928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017308,"about_ca_topic_score_gemma":0.003990072,"domain_scores_codex":[0.9993953,0.0002283185,0.00003598011,0.0001121203,0.00008963382,0.0001387517],"domain_scores_gemma":[0.9961336,0.002050692,0.0009825097,0.000263279,0.0002273846,0.0003425324],"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":[0.0003060745,0.0001661374,0.01280092,0.0001126042,0.0001335939,0.0005662217,0.0004576098,0.4903002,0.0005735566,0.4674351,0.006463659,0.02068445],"study_design_scores_gemma":[0.00006646604,0.0000318503,0.002055485,0.00001715618,0.00002284324,0.00004816752,0.00007906606,0.8581511,0.00009348504,0.1380909,0.001321863,0.00002163423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7347869,0.002086658,0.2211232,0.008236068,0.0002811404,0.0001418187,0.001612091,0.0004409496,0.03129118],"genre_scores_gemma":[0.9832373,0.0006615709,0.00216168,0.0001937613,0.0001379316,0.00004066019,0.0003151431,0.00003234614,0.01321969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01017308,"threshold_uncertainty_score":0.03371173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1249336286844082,"score_gpt":0.1767824193778812,"score_spread":0.05184879069347298,"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."}}