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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.
