CHAPTER 16
TEACHING NOTES
I spend some time in Section 16.1 trying to distinguish between good and inappropriate uses of
SEMs.
Naturally, this is partly determined by my taste, and many applications fall into a gray
area.
But students who are going to learn about SEMS should know that just because two (or
more) variables are jointly determined does not mean that it is appropriate to specify and
estimate an SEM.
I have seen many bad applications of SEMs where no equation in the system
can stand on its own with an interesting ceteris paribus interpretation. In most cases, the
researcher either wanted to estimate a tradeoff between two variables, controlling for other
factors – in which case OLS is appropriate – or should have been estimating what is (often
pejoratively) called the “reduced form.”
The identification of a two-equation SEM in Section 16.3 is fairly standard except that I
emphasize that identification is a feature of the population.
(The early work on SEMs also had
this emphasis.)
Given the treatment of 2SLS in Chapter 15, the rank condition is easy to state
(and test).
Romer’s (1993) inflation and openness example is a nice example of using aggregate cross-
sectional data.
Purists may not like the labor supply example, but it has become common to
view labor supply as being a two-tier decision.
While there are different ways to model the two
tiers, specifying a standard labor supply function conditional on working is not outside the realm
of reasonable models.
Section 16.5 begins by expressing doubts of the usefulness of SEMs for aggregate models such
as those that are specified based on standard macroeconomic models.
Such models raise all
kinds of thorny issues; these are ignored in virtually all texts, where such models are still used to
illustrate SEM applications.
SEMs with panel data, which are covered in Section 16.6, are not covered in any other
introductory text.
Presumably, if you are teaching this material, it is to more advanced students
in a second semester, perhaps even in a more applied course.
Once students have seen first
differencing or the within transformation, along with IV methods, they will find specifying and
estimating models of the sort contained in Example 16.8 straightforward.
Levitt’s example
concerning prison populations is especially convincing because his instruments seem to be truly
exogenous.
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