{"id":"W2797698245","doi":"10.5539/ijef.v10n5p184","title":"The Impact of RMB Exchange Rate Fluctuation on Price Level in China: An Empirical Analysis Based on the Vector Error Correction Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Renminbi; Exchange rate; Economics; Error correction model; Effective exchange rate; China; Index (typography); Econometrics; Monetary economics; Price index; Price level; Transfer (computing); Consumer price index (South Africa); International economics; Cointegration; Monetary policy; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001267897,0.0001401952,0.0003139469,0.0003903941,0.0000879465,0.00007702131,0.0003914759,0.00006769461,0.00005247987],"category_scores_gemma":[0.0001523445,0.0001067369,0.0001782304,0.0001381886,0.0001159296,0.0002644256,0.00003341083,0.0001757737,0.00001092778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004237638,"about_ca_system_score_gemma":0.00005231089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001544815,"about_ca_topic_score_gemma":0.0002195522,"domain_scores_codex":[0.9987616,0.00003817237,0.0007724455,0.0002413819,0.00003606331,0.0001503341],"domain_scores_gemma":[0.9982497,0.000196,0.001220703,0.0002388218,0.00005825466,0.00003652072],"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":[0.0004120407,0.0001965438,0.02321452,0.00000112001,0.0002657128,0.000001094915,0.000364805,0.9400836,0.00001654336,0.03326883,0.0002863704,0.001888816],"study_design_scores_gemma":[0.0003655761,0.0002879748,0.3402501,0.000008161701,0.000006907938,0.000001701073,0.00001133364,0.6514406,0.00007344936,0.007078414,0.0003995213,0.00007625465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922036,0.00007934701,0.003738564,0.001799432,0.0005489311,0.00008145407,0.0001125119,0.000001601148,0.001434593],"genre_scores_gemma":[0.9981929,0.0009770346,0.000228523,0.000242324,0.0001884373,0.000007095557,0.000007004764,0.00001412335,0.0001425523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3170356,"threshold_uncertainty_score":0.4352603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05338098944494561,"score_gpt":0.2793427897133887,"score_spread":0.2259618002684431,"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."}}