{"id":"W4307701659","doi":"10.3390/jrfm15110499","title":"Exchange Rate Volatility Effect on Economic Growth under Different Exchange Rate Regimes: New Evidence from Emerging Countries Using Panel CS-ARDL Model","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Exchange rate; Exchange-rate flexibility; Volatility (finance); Autoregressive conditional heteroskedasticity; Econometrics; Granger causality; Effective exchange rate; Monetary economics; Emerging markets; Variance decomposition of forecast errors; Conditional variance; Exchange-rate regime; Macroeconomics","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":[0.001301516,0.0003418381,0.0003969506,0.0009667446,0.0002658019,0.001150783,0.000261074,0.0003076222,0.001239636],"category_scores_gemma":[0.002807925,0.0001294333,0.0008736428,0.001631043,0.0003555593,0.0008028965,0.0008101229,0.0009296325,0.0002582087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537765,"about_ca_system_score_gemma":0.0002583737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009414403,"about_ca_topic_score_gemma":0.008462399,"domain_scores_codex":[0.9995946,0.0001479895,0.00003690526,0.00009013985,0.00006082967,0.00006957282],"domain_scores_gemma":[0.9956463,0.001867502,0.001467303,0.0003979953,0.0003507767,0.0002702256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005229031,0.0001747239,0.9629148,0.00008743206,0.0006358323,0.0009322459,0.0004224932,0.01447252,0.0005799912,0.00303869,0.001709673,0.01450867],"study_design_scores_gemma":[0.00004945722,0.0003066112,0.9605042,0.00006944084,0.0005837401,0.000332796,0.001457265,0.03040335,0.0009810576,0.001663271,0.003605688,0.00004321996],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994957,0.0006024345,0.00107714,0.0001562242,0.00001544987,0.000005419326,0.001117182,0.00001459173,0.002054588],"genre_scores_gemma":[0.996473,0.0005543961,0.0002386838,0.00002922389,0.00002129377,0.000003656592,0.002369472,0.000004292056,0.0003060429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009414403,"threshold_uncertainty_score":0.0187192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08053695637634484,"score_gpt":0.2448640127181221,"score_spread":0.1643270563417772,"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."}}