Big Data: New Tricks for Econometrics

TLDR

Computers now capture data from many economic transactions, and while conventional econometric techniques often work, big datasets require more powerful manipulation tools, variable selection, and flexible models, prompting the use of machine learning methods such as decision trees, support vector machines, neural nets, and deep learning. The essay aims to describe tools for manipulating and analyzing big data. It discusses machine learning techniques—including decision trees, support vector machines, neural nets, and deep learning—as tools for this manipulation and analysis. The author concludes that these methods are valuable and should be more widely known and used by economists.

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.