Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/193433 
Year of Publication: 
2018
Citation: 
[Journal:] IZA World of Labor [ISSN:] 2054-9571 [Article No.:] 451 [Publisher:] Institute of Labor Economics (IZA) [Place:] Bonn [Year:] 2018
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.
Subjects: 
Big Data
machine learning
prediction
causal inference
JEL: 
C55
C8
Persistent Identifier of the first edition: 
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.