The Journal of Economic Perspectives· 2014Q1
Big Data: New Tricks for Econometrics
- 1,556citations
- Q1SCImago
- 2014year
Short summary
Economists can leverage machine learning techniques like decision trees, support vector machines, and neural networks to analyze big data, moving beyond conventional regression.
AI-generated from the title and abstract; the full text is not read.
Key points
- Big data from economic transactions requires specialized tools beyond conventional regression.
- Machine learning techniques (decision trees, SVMs, neural nets) can model complex relationships.
- These methods address challenges of large data size and numerous potential predictors.
- Economists are encouraged to adopt these advanced analytical tools.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. The Journal of Economic Perspectives, 2014 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science