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dc.creatorBluhm, Benjamin
dc.creatorCutura, Jannic
dc.date.accessioned2021-09-28T09:39:04Z
dc.date.available2021-09-28T09:39:04Z
dc.date.issued2020-02-06
dc.identifier.urihttps://fif.hebis.de/xmlui/handle/123456789/2372
dc.description.abstractThis paper provides an overview of how to use “big data” for economic research. We investigate the performance and ease of use of di?erent Spark applications running on a distributed ?le system to enable the handling and analysis of data sets which were previously not usable due to their size. More speci?cally, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical econometric tasks including microeconometric, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.
dc.rightsAttribution-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.subjectFinancial Intermediation
dc.titleEconometrics at Scale: Spark Up Big Data in Economics
dc.typeWorking Paper
dc.source.filename266_SSRN-id3226976
dc.identifier.safeno266
dc.subject.keywordseconometrics
dc.subject.keywordsdistributed computing
dc.subject.keywordsapache spark
dc.subject.jelC53
dc.subject.jelC55
dc.subject.topic1machine
dc.subject.topic1wellKnown
dc.subject.topic1economic
dc.subject.topic2paper
dc.subject.topic2preProcessing
dc.subject.topic2key
dc.subject.topic3specification
dc.subject.topic3basic
dc.subject.topic3easily
dc.subject.topic1nameConsumption
dc.subject.topic2nameFiscal Stability
dc.subject.topic3nameSystematic Risk
dc.identifier.doi10.2139/ssrn.3226976


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