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Data in econometrics: variables, samples & populations

Published on September 30, 2026   9 min

A selection of talks on Finance, Accounting & Economics

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0:00
Welcome back, everyone. My name is Alessio Volpicella. I am an associate professor of economics at the University of Pavia, Italy. Today we're going to continue our journey on an introduction to econometrics. In particular, we're going to have a look at data in econometrics in terms of variables, samples, and population, different typologies of economic data we might have and why that matters.
0:37
We're going to build on what we saw in the previous lecture where we argued that correlation doesn't imply causation, and mostly the reason is that, you know economic data don't allow lab-style experiments. Economics, including econometrics, is not like hard science. You know, economies, we argue, are open systems. They are affected by many confounding factors we can't observe, so we cannot really replicate economies in a lab. And from that point of view, we started looking into some techniques helping uncover true causal relationships.
1:20
The plan for today, as I mentioned, is looking into typologies of data in econometrics. We're going to define some key terms and, most importantly, why data quality and sampling affect what we can conclude about the relationship between economic variables. So as a running example today, let's assume you are a health economist working on the economic effect of pneumonia in the UK. First of all, you might want to collect data about a patient's features including age, gender, employment, economic background, and so on.

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Data in econometrics: variables, samples & populations

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