{"title":"基于分类数据的密度预测:临近预测波兰通货膨胀","authors":"Błażej Mazur","doi":"10.12775/DEM.2015.004","DOIUrl":null,"url":null,"abstract":"The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of exclusion restrictions. Empirical analysis of Polish CPI m-o-m inflation rate (using its 14 sub-categories for disaggregate modelling) is presented. Exclusion restrictions are shown to improve density forecasting performance (as evaluated using log-score and CRPS criteria) relatively to aggregate and also disaggregate unrestricted models.","PeriodicalId":31914,"journal":{"name":"Dynamic Econometric Models","volume":"15 1","pages":"71-87"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Density forecasts based on disaggregate data: nowcasting Polish inflation\",\"authors\":\"Błażej Mazur\",\"doi\":\"10.12775/DEM.2015.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of exclusion restrictions. Empirical analysis of Polish CPI m-o-m inflation rate (using its 14 sub-categories for disaggregate modelling) is presented. Exclusion restrictions are shown to improve density forecasting performance (as evaluated using log-score and CRPS criteria) relatively to aggregate and also disaggregate unrestricted models.\",\"PeriodicalId\":31914,\"journal\":{\"name\":\"Dynamic Econometric Models\",\"volume\":\"15 1\",\"pages\":\"71-87\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Dynamic Econometric Models\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12775/DEM.2015.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dynamic Econometric Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12775/DEM.2015.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Density forecasts based on disaggregate data: nowcasting Polish inflation
The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of exclusion restrictions. Empirical analysis of Polish CPI m-o-m inflation rate (using its 14 sub-categories for disaggregate modelling) is presented. Exclusion restrictions are shown to improve density forecasting performance (as evaluated using log-score and CRPS criteria) relatively to aggregate and also disaggregate unrestricted models.