117
CHAPTER 10
TEACHING NOTES
Because of its realism and its care in stating assumptions, this chapter puts a somewhat heavier
burden on the instructor and student than traditional treatments of time series regression.
Nevertheless, I think it is worth it.
It is important that students learn that there are potential
pitfalls inherent in using regression with time series data that are not present for crosssectional
applications.
Trends, seasonality, and high persistence are ubiquitous in time series data.
By
this time, students should have a firm grasp of multiple regression mechanics and inference, and
so you can focus on those features that make time series applications different from cross
sectional ones.
I think it is useful to discuss static and finite distributed lag models at the same time, as these at
least have a shot at satisfying the GaussMarkov assumptions.
Many interesting examples have
distributed lag dynamics.
In discussing the time series versions of the CLM assumptions, I rely
mostly on intuition.
The notion of strict exogeneity is easy to discuss in terms of feedback.
It is
also pretty apparent that, in many applications, there are likely to be some explanatory variables
that are not strictly exogenous.
What the student should know is that, to conclude that OLS is
unbiased – as opposed to consistent – we need to assume a very strong form of exogeneity of the
regressors.
Chapter 11 shows that only contemporaneous exogeneity is needed for consistency.
Although the text is careful in stating the assumptions, in class, after discussing strict exogeneity,
I leave the conditioning on
X
implicit, especially when I discuss the no serial correlation
assumption. As the absence of serial correlation is a new assumption I spend a fair amount of
time on it.
(I also discuss why we did not need it for random sampling.)
Once the unbiasedness of OLS, the GaussMarkov theorem, and the sampling distributions under
the classical linear model assumptions have been covered – which can be done rather quickly – I
focus on applications.
Fortunately, the students already know about logarithms and dummy
variables.
I treat index numbers in this chapter because they arise in many time series examples.
A novel feature of the text is the discussion of how to compute goodnessoffit measures with a
trending or seasonal dependent variable.
While detrending or deseasonalizing
y
is hardly perfect
(and does not work with integrated processes), it is better than simply reporting the very high
R

squareds that often come with time series regressions with trending variables.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document