{"id":"W1863543355","doi":"10.5539/ijef.v7n11p121","title":"Exchange Rate Volatility and Trade Flows: Evidence from China, Pakistan and India","year":2015,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Volatility (finance); Exchange rate; Econometrics; Short run; China; Distributed lag; Autoregressive model; Monetary economics; Geography","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":[0.0003181497,0.0001871616,0.000168214,0.001639589,0.000387854,0.000791254,0.0001846338,0.0001843926,0.001154276],"category_scores_gemma":[0.001144618,0.0001103739,0.000298181,0.003282464,0.0003887502,0.0004043489,0.0004641478,0.0002975281,0.0001267135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271439,"about_ca_system_score_gemma":0.0005531299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03247182,"about_ca_topic_score_gemma":0.03554721,"domain_scores_codex":[0.9998462,0.00002132151,0.00001620939,0.00002557509,0.00005160966,0.00003905466],"domain_scores_gemma":[0.9983836,0.0004054872,0.0007975905,0.00008458032,0.0001718094,0.0001567622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007707413,0.00002456861,0.991142,0.00005660399,0.0001055418,0.0005198402,0.0009045853,0.0003404485,0.0002258714,0.0005438991,0.0004168221,0.005642656],"study_design_scores_gemma":[0.000003618936,0.00002083897,0.9977013,0.00001475122,0.0000441742,0.0001705615,0.0008801883,0.0002175467,0.0001064234,0.00006242097,0.0007724544,0.000005548932],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975084,0.0003632762,0.00002699617,0.0001228243,0.000003245284,0.000002287558,0.0004714828,0.000001895291,0.001499641],"genre_scores_gemma":[0.9981716,0.0007004802,0.00002597204,0.00001972347,0.00001025203,0.000001314268,0.0008346766,9.279601e-7,0.0002350794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03247182,"threshold_uncertainty_score":0.06456566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08451893010617405,"score_gpt":0.2634347848777941,"score_spread":0.1789158547716201,"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."}}