Economics is not an exact science, but it does deal with numbers and data. A lot of numbers and data. Big data. That’s why the course called “Econometrics” (which literally means “economic measures”) takes place in the SSE-Riga Year 2 curriculum.

Economic theory makes statements that are usually qualitative, but the theory itself does not provide any numerical outcomes. It is the job of the econometrician to confront theory against data and to provide numerical estimates.

The econometrics course introduces students to a set of Machine Learning techniques that provide numerical answers using available data.

Although most students will be consumers of econometric and statistical information in their professional life, an understanding of econometrics will teach students how to understand and critically evaluate empirical studies in economics and related fields. Even more, the functioning of many modern Machine Learning algorithms will no longer look like black magic.

Of course, a theoretical knowledge of formulas is not enough. You cannot learn swimming through theory. That’s why a lot of time is devoted to practical analysis of real economic data using the extremely popular statistical package ‘R. Remember’ – future employers love people who are familiar with Data Science and have programming skills in R.

You can predict the inflation rate in the next month, you can evaluate real estate without inviting an expert; you can even tell the probability of Di Caprio surviving the sinking of The Titanic. All you need is data and a knowledge of Econometrics.



Student Dans says about the course:

“If I had to name the single course that has had the most profound impact on my career aspirations, it would be Econometrics. Econometrics is probably the most technical course at the school, but its "price tag" in terms of effort is negligible compared to the value the course enables one to deliver afterwards.

An understanding of how to approach data and how to avoid spurious conclusions are very much in demand, coupled with a portfolio of machine learning techniques, altogether a starter pack for a career in data analytics.

This path – a path that I have started pursuing – is full of further learning and specialisation opportunities. But the very foundations, masterfully taught by Konstantīns Beņkovskis, are something I keep returning to when faced by a challenging analytical task. Ultimately, Econometrics is all about scientific thinking that cannot be easily developed outside university.”