Econometrics and Machine Learning

Both econometrics and machine learning provide a way for analysts to have a glimpse of the future, and on that basis, make predictions. As research methodologies, both strive towards the same goal: inducing new knowledge. And, for this purpose, they adopt statistical tools, making for precision in scientific research.

However, although they share similarities, they also have their differences. An in-depth look at the two will reveal more.

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What Is Econometrics?

Econometrics is an “economics” term that describes the quantitative application of mathematical and statistical models. Econometrics is useful for developing theories, testing existing ones, and predicting the future. For these functions, it takes into account theoretical historical data, tests them using statistical methods and tools, and then compares the results.

Econometrics leverage such statistical tools as probability and probability distributions, frequency distributions, statistical inference, simple and multiple regression analysis. In addition, correlation analysis, simultaneous equations models, and time series methods are used in the process.

Econometrics was the brainwork of three award-winning economists; Ragnar Frisch, Lawrence Klein, and Simon Kuznets. And, since its inception, many academics, researchers, and analysts have found this concept a worthy methodology for making inferences and forecasts.

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) that studies how systems can imitate human learning using data. It focuses on how computer algorithms can automatically make decisions and improve in decision-making without human assistance (no explicit programming), but relying on data provided.

The origin of the term “machine learning” is accredited to Arthur Samuel, who pioneered Artificial Intelligence and computer gaming.

As an important part of the growing field of data science, machine learning uses statistical methods to train algorithms to make predictions. These algorithms can also provide basic knowledge that facilitates decision-making in businesses and applications, thereby impacting key growth metrics.

In machine learning, the choice of algorithms to be used is a function of the available data and the activity that needs to be automated.

Econometrics and Machine Learning

Econometrics and machine learning share a philosophical underpinning, as well as purpose. Both rely on statistical inferences; using mathematical tools to investigate hard data, to the end of generating new knowledge.

Since econometrics and machine learning takes the same research approach, they share the same risks and limitations. For instance, they are both prone to bias and overfitting, and the quality of input data can affect results as well, explaining the “garbage in, garbage out” idea.

However, in differentiating the two, econometrics as a body of knowledge, has its relevance in the economic field of study. It was developed to address economic (financial) drawbacks and so, economic data is used. Machine learning on the other hand goes beyond the financial system, it is practically not committed to any specific field of application.

Secondly, econometrics tends to assimilate economic background knowledge more easily, using it to estimate and forecast. This may not be so much of a smooth process in machine learning, hence background knowledge is fitted more into the data preparation phase, in heuristics, and technique-specific characterization.

This may result in trial-and-error “data torture” and will most likely affect results, although this may depend largely on the practitioner’s ethics.

Nonetheless, both can share and benefit from each other sampling strategies, cross-validation, and model ensemble techniques, information compressing, model assessment.


Although econometrics and machine learning share the same research tools, they differ in what they seek to achieve.

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