Big Data in economics
Jonathan Hersh and
Matthew Harding ()
IZA World of Labor, 2018, No 451, 451
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.
Keywords: Big Data; machine learning; prediction; causal inference (search for similar items in EconPapers)
JEL-codes: C55 C8 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:iza:izawol:journl:2018:n:451
Access Statistics for this article
IZA World of Labor is currently edited by Pierre Cahuc
More articles in IZA World of Labor from Institute of Labor Economics (IZA) IZA, P.O. Box 7240, D-53072 Bonn, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Institute of Labor Economics (IZA) ().