Replication data for: Big Data: New Tricks for Econometrics

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Project Citation:

Varian, Hal R. Replication data for: Big Data: New Tricks for Econometrics. Nashville, TN: American Economic Association [publisher], 2014. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113925V1

Project Description

Summary: 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.

Scope of Project

Subject Terms: Causal Inference; Big Data; Machine Learning

JEL Classification:
C55 Large Data Sets: Modeling and Analysis

Time Period(s): 5/1/2014 – 5/1/2014

Universe: Example datasets to illustrate the methodology

Data Type(s): survey data; census/enumeration data; observational data; program source code; aggregate data

Methodology

Data Source: Misc

Unit(s) of Observation: Miscellaneous,

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Published Versions

V1 [2019-10-12]

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Dublin Core

DDI 2.5

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