{"id":"W1483935963","doi":"10.24006/jilt.2007.5.2.006","title":"Estimation of the J-Curve in China: a cointegration approach","year":2007,"lang":"en","type":"article","venue":"Journal of International Logistics and Trade","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cointegration; Economics; Depreciation (economics); Balance of trade; Econometrics; Exchange rate; China; Estimation; Bilateral trade; Short run; Balance (ability); Series (stratigraphy); Macroeconomics; Monetary economics; Microeconomics","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":[0.003009339,0.0004857523,0.0008093443,0.003393349,0.0004775717,0.0009913472,0.0006770848,0.0008367933,0.00175648],"category_scores_gemma":[0.01179815,0.0003188058,0.0008798674,0.003632877,0.0005605881,0.001328728,0.0007719796,0.0006752715,0.0002904252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006760625,"about_ca_system_score_gemma":0.00141777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02217326,"about_ca_topic_score_gemma":0.008386117,"domain_scores_codex":[0.9990975,0.0004238793,0.0000541082,0.0001702718,0.0001361683,0.000118219],"domain_scores_gemma":[0.9964808,0.001979207,0.0005827019,0.0003414338,0.0004888123,0.0001271511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003274609,0.0002191759,0.4118255,0.0002917436,0.0005728428,0.001506326,0.001067126,0.3816004,0.00563731,0.04137672,0.002470162,0.1531053],"study_design_scores_gemma":[0.00002588322,0.0001146071,0.114407,0.00003082104,0.00006401664,0.00009271155,0.0002677116,0.8743166,0.0009997742,0.008156127,0.001487858,0.00003685919],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371556,0.0004681846,0.05905124,0.00028095,0.00002023959,0.00006035613,0.0002259632,0.0001392481,0.002598336],"genre_scores_gemma":[0.9926655,0.0002315072,0.00619974,0.00001497312,0.00001411698,0.00001867377,0.0003203174,0.00001787229,0.0005173071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02217326,"threshold_uncertainty_score":0.04408836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05297065514043781,"score_gpt":0.2468508971894572,"score_spread":0.1938802420490194,"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."}}