{"id":"W2934556898","doi":"10.2139/ssrn.3360721","title":"Macroeconomic Shocks and Trade Balance Adjustments in Papua New Guinea","year":2019,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"International Development Research Centre","funders":"","keywords":"Economics; Exchange rate; Balance of trade; Terms of trade; Boom; Devaluation; Inflation (cosmology); Endogeneity; Monetary economics; International economics; Bayesian vector autoregression; Macroeconomics; Econometrics; Bayesian probability","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":[0.0002731015,0.0001299584,0.0001762436,0.0005052318,0.0003348936,0.000963457,0.0002586263,0.000399432,0.003168505],"category_scores_gemma":[0.001097703,0.0001262535,0.0002088578,0.0008154121,0.0003601886,0.0006185957,0.0005819799,0.0004603423,0.000204079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108658,"about_ca_system_score_gemma":0.0004779891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04827394,"about_ca_topic_score_gemma":0.04141338,"domain_scores_codex":[0.9999421,0.00001241432,0.000004989449,0.00001167835,0.000004232807,0.00002448274],"domain_scores_gemma":[0.9997453,0.00007391707,0.0001059765,0.00001427609,0.00002631596,0.00003416322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001252821,0.000226688,0.9220557,0.0001653797,0.0003201966,0.003293304,0.003218637,0.01382409,0.00435156,0.01427498,0.002254577,0.03476207],"study_design_scores_gemma":[0.00004001266,0.00008198828,0.9807587,0.00006285108,0.00009016097,0.000191031,0.002852202,0.008517289,0.0005433136,0.003494477,0.003349958,0.00001809247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979242,0.0001269757,0.00007086815,0.0002629934,0.00001212924,0.000002677869,0.0001710256,0.000003874785,0.001425335],"genre_scores_gemma":[0.9991799,0.0001218715,0.00004874473,0.00002537226,0.000006595045,0.000002710786,0.00008679144,0.000002052478,0.0005260092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04827394,"threshold_uncertainty_score":0.09598589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409216813521483,"score_gpt":0.2105967427393294,"score_spread":0.1965045746041146,"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."}}