- [Narrator] What's the difference between econometrics and data science?
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- [Josh] I would say that the principal difference
is the approach to the problem of prediction.
Data scientists are often concerned
with curve fitting type approaches to prediction.
So any model that fits the data will do.
If it's past experience, we might be interested in using that
to extrapolate to the future.
A lot of the data science agenda is tied
to somebody's marketing problems.
You're trying to figure out who will buy something,
who will take some action.
Econometrics in my view deals
with kind of a harder class of problems.
Econometricians are more concerned with causal relationships.
In other words, if we manipulate something,
say, health insurance or monetary policy,
what's the world going to look like in response to that change.
We don't take it for granted that the past is a good guide to that
because we understand that variation and variable is associated
with lots of potential confounding variables,
we would say other things that are moving
that also perhaps affect outcome.
The simple observed relationship there is often misleading
because there are factors that are not well controlled,
and we have in mind that there is a research design
that involves more than curve fitting.
In fact, we're fairly indifferent to curve fitting in economics.
I think we want to know, for example, whether it matters
if you go to an expensive private college --
does that change your life course in the form of higher earnings?
That's not really a curve fitting question,
that's a causal question.
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- [Narrator] Ready to master econometrics?
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