Would technologies as AI, BI and Big Data help the economists to make better predictions and test models to solve problems like "What level of fiscal stimulus is better for a especific country?"

Is it something for the future or its already been used?

  • $\begingroup$ I think this question is to vague, so the answer can be only yes and no. For sure the new technology helps in many situations and for a lot of problems, but on the other side it puts itself another layer of complexity into the economic world. And it is in my opinion questionable if a predictable future is a value in itself? It would possible look like the Brave New World. $\endgroup$
    – giftnuss
    Dec 20, 2017 at 14:32
  • $\begingroup$ This question is too vague. Are you familiar with methods currently employed? $\endgroup$
    – EconJohn
    Dec 21, 2017 at 21:35
  • $\begingroup$ @EconJohn No I'm not familiar, I'm not an economist, just a curious man that want to understand things better. How can I make my question better? I'd like to know those tecnologies would help economist to simulate situations and try to make better predictions about economy, something like metereologists do with computer simulators to predict climate changes. $\endgroup$ Dec 22, 2017 at 1:21
  • $\begingroup$ @GuilhermedeJesusSantos Ah I see. $\endgroup$
    – EconJohn
    Dec 22, 2017 at 4:05

2 Answers 2


I'm an economist by training who also is a programmer and works with a lot of data scientists, so I've some insight into this area. One of my projects at the Urban Institute is trying to bridge the gap between these fields, and part of our work, supported by the Sloan Foundation, is publicly available here.

To some degree the answer is yes, but there are huge obstacles as well. Some are surmountable with work, like differences in terminology, programming platforms (knowledge of SAS or Stata versus R or Python) and conventions (do others in your field understand and/or accept the results of machine learning models?). Others are much more difficult to surmount, such as the size of data available.

Essentially, the fields that make extensive use of AI and machine learning have huge amounts of data. They can afford to, say, set aside a million observations for training the model and then test the model on the other nine million. Economists, and social scientists in general, however, are often lucky if they have 100 observations. Many macroeconomic variables are annual, or quarterly. Some are monthly, which means if you can get 30 years of observations then you've got 360 time periods! Data-rich by social science standards, but not even a rounding error in many data science models.

That said, computing power and sophisticated data collection are making it more and more feasible to approach social science questions with big data. Social media is a major area that social scientists are just beginning to reach into. However, some areas of economics will likely remain closed off to data science methods due to the nature of the data. Your fiscal stimulus example might very well be one - the relevant data just isn't produced that fast.

So the answer to your question is yes, with reservations and a hope for the future.


There seems to be a lot of misconceptions regarding the use of technology in economics. Its important to remember a two points.

1. Modern economic theory is mostly, just a branch of Applied Math
Sure there are lots of fundamental philosophical concepts underlying the economics as a social science, however most practical problems require some calculus or linear algebra.

2. Economists test theories all the time using statistics.
Some people don't know how data driven economics is.

The utilization of Vector Auto Regressive Models (VAR),Vector Error Correction Models (VECM) and Impulse Response Functions in Macroeconomics tests is common practice. Central Banks use these methods and are constantly testing economic theories all the time.

Answering the question: "What level of fiscal stimulus is better for a specific country?" has already been considering these methods for a while and are not a result of the big data revolution.

Hope this helps


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