{"id":"W1511005973","doi":"","title":"Explaining Government Spending: a Cointegration Approach","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Fiscal Policies and Political Economy","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cointegration; Economics; Government (linguistics); Social security; Government spending; Population; Ideology; Government expenditure; Public economics; Public expenditure; Macro; Public finance; Econometrics; Macroeconomics; Political science; Politics; Market economy; Sociology; Demography; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001016382,0.0007541573,0.001081767,0.002496755,0.000432996,0.001643044,0.0006134228,0.001207,0.008274415],"category_scores_gemma":[0.005981379,0.00042347,0.0009060584,0.004763208,0.0007605779,0.001546543,0.0008622606,0.001162059,0.001239014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008700726,"about_ca_system_score_gemma":0.001099905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0107878,"about_ca_topic_score_gemma":0.007080705,"domain_scores_codex":[0.9994397,0.0002879359,0.00002404779,0.00007932401,0.00005803662,0.0001109582],"domain_scores_gemma":[0.9980308,0.001451151,0.0002513894,0.0001208074,0.00009767278,0.00004808969],"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":[0.00006887475,0.0001639124,0.03336607,0.0001779329,0.0003523056,0.0004204907,0.0004152317,0.4792559,0.0007141084,0.4325509,0.006620349,0.04589398],"study_design_scores_gemma":[0.00003695957,0.00006542353,0.008101599,0.00008363241,0.0001455764,0.00008881144,0.0001763555,0.7807229,0.0002758088,0.2035414,0.006735201,0.0000263868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.451414,0.004498807,0.5137508,0.006489427,0.0002035314,0.0001375243,0.001757334,0.001044329,0.02070425],"genre_scores_gemma":[0.9724756,0.004329436,0.01400857,0.0001706774,0.0002437158,0.0001281825,0.00113043,0.00008262995,0.007430638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0107878,"threshold_uncertainty_score":0.02768064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06556030061393363,"score_gpt":0.2970310807234803,"score_spread":0.2314707801095466,"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."}}