Massimiliano Marcellino, Fotis Papailias, G. Mazzi, G. Kapetanios, Dario Buono
{"title":"Big Data Econometrics: Now Casting and Early Estimates","authors":"Massimiliano Marcellino, Fotis Papailias, G. Mazzi, G. Kapetanios, Dario Buono","doi":"10.2139/ssrn.3206554","DOIUrl":null,"url":null,"abstract":"This paper aims at providing a primer on the use of big data in macroeconomic nowcasting and early estimation. We discuss: (i) a typology of big data characteristics relevant for macroeconomic nowcasting and early estimates, (ii) methods for features extraction from unstructured big data to usable time series, (iii) econometric methods that could be used for nowcasting with big data, (iv) some empirical nowcasting results for key target variables for four EU countries, and (v) ways to evaluate nowcasts and ash estimates. We conclude by providing a set of recommendations to assess the pros and cons of the use of big data in a specific empirical nowcasting context.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PSN: Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3206554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
This paper aims at providing a primer on the use of big data in macroeconomic nowcasting and early estimation. We discuss: (i) a typology of big data characteristics relevant for macroeconomic nowcasting and early estimates, (ii) methods for features extraction from unstructured big data to usable time series, (iii) econometric methods that could be used for nowcasting with big data, (iv) some empirical nowcasting results for key target variables for four EU countries, and (v) ways to evaluate nowcasts and ash estimates. We conclude by providing a set of recommendations to assess the pros and cons of the use of big data in a specific empirical nowcasting context.