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Replication data for: Big Data: New Tricks for Econometrics

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  • Varian, Hal R.
Publication Date
  • Abstract

    Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.
  • Is supplement to
    DOI: 10.1257/jep.28.2.3 (Text)
  • Varian, Hal R. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives 28, no. 2 (May 2014): 3–28.
    • ID: 10.1257/jep.28.2.3 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Varian, Hal R. (2014): Replication data for: Big Data: New Tricks for Econometrics. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset.