Lecture 1:
Introduction to econometrics
BUEC 333
Professor David Jacks
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Start with a definition: the application of
economic intuition/theory and statistical methods
to analyze economic data.
Emphasis of most courses in economics on
theory (and maybe a little intuition); in this
course, we focus on statistical methods.
In particular, their application to real-world data
problems encountered in academic, industry, and
policy settings.
What exactly is econometrics?
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Need to recognize that econometrics is both a
science and an art.
Economic theory and statistics are grounded in
mathematics—the “scientific” side.
But there is always a disconnect between
economic theory and the real world.
Data is more often than not messy and requires
finesse—the “artistic” side along with intuition.
What exactly is econometrics?
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Generally, we are interested in measuring or
estimating particular economic relationships.
From ECON 101, what is the price elasticity of
demand for LED TVs?
Manufacturers and retailers wants to know this
variable when setting their prices.
This could be—and is—estimated using
econometrics and retail data; the rise of Amazon.
Empirical questions demand empirical answers
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From labor economics, does reducing class size
improve the quality of education?
Smaller classes = more attention = more learnin’
But how much more and is it worth it?
Exploit variation in test scores and class sizes.
Complications: what do scores truly measure?
And does class size cause higher test scores or
just correlated with some other variable?
Empirical questions demand empirical answers
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From finance, what will be the value of the S&P/
TSX index one quarter from today?
Knowing with certainty worth a lot of money.
But even knowing with some uncertainty is also
valuable to portfolio managers.
A pure forecasting problem: estimate the future
value of the index based on its historical
behavior and relation to economic fundamentals.
Empirical questions demand empirical answers
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The purpose of this class is for you to learn the
statistical methods you would use to answer
these questions…and many, many more.
You will also learn how to assess whether a
particular estimate/analysis is any good.
You will learn to use a specific statistical
software package (Stata) and get some
experience with real data.
What you will (hopefully) learn in BUEC 333
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Academically, it is useful for future studies here
at SFU (4th year courses) and grad school.
Professionally, the past 10 years have seen a
massive increase in data collection in industry;
Big Data: source of FB/LI’s $300 b. valuation.
Using that data will be a growth industry for the
next ten years; New York Times, “For Today’s
Graduate, Just One Word: Statistics”.
Why should you care?
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Even if doing something completely different, it
is essential for understanding the broader world.
This course will help you understand where
estimates come from, how reliable they are, and
what they do and do not actually mean…
a valuable BS detector.
This type of information should then guide your
decisions as an individual consumer, investor,
manager, voter…
Why should you care?
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But we must begin with a review of statistics.
Things you hopefully learned in BUEC 232,
but probably forgot in the meantime:
1.) random variables
2.) probabilities
3.) probability distributions
4.) expected values
5.) variances
Next steps forward
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