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CHAPTER 9
TEACHING NOTES
The coverage of RESET in this chapter recognizes that it is a test for neglected nonlinearities,
and it should not be expected to be more than that.
(Formally, it can be shown that if an omitted
variable has a conditional mean that is linear in the included explanatory variables, RESET has
no ability to detect the omitted variable. Interested readers may consult my chapter in
Companion to Theoretical Econometrics
, 2001, edited by Badi Baltagi. Alternatively, Problem
9.8 essentially contains a proof of this claim.) I just teach students the
F
statistic version of the
test.
The Davidson-MacKinnon test can be useful for detecting functional form misspecification,
especially when one has in mind a specific alternative, nonnested model.
It has the advantage of
always being a one degree of freedom test.
I think the proxy variable material is important, but the main points can be made with Examples
9.3 and 9.4.
The first shows that controlling for IQ can substantially change the estimated return
to education, and the omitted ability “bias” is in the expected direction. (Controlling for IQ
lowers the return to education.) Interestingly, education and ability do not appear to have an
interactive effect.
Example 9.4 is a nice example of how controlling for a previous value of the
dependent variable – something that is often possible with survey and nonsurvey data – can
greatly affect a policy conclusion.
Computer Exercise 9.3 is also a good illustration of this
approach.
The short section on random coefficient models is intended as a simple introduction to the idea;
it is not needed for any other parts of the book.
I rarely get to teach the measurement error material in a first-semester course, although the
attenuation bias result for classical errors-in-variables is worth mentioning. You might have
analytically skilled students try Problem 9.7.
The result on exogenous sample selection is easy to discuss, with more details given in Chapter
17. The effects of outliers can be illustrated using the examples. I think the infant mortality
example, Example 9.10, is useful for illustrating how a single influential observation can have a
large effect on the OLS estimates. Studentized residuals are computed by many regression
packages, and they can be informative if used properly.
With the growing importance of least absolute deviations, it makes sense to discuss the merits of
LAD, at least in more advanced courses. Computer Exercise C9.9 is a good example to show
how mean and median effects can be very different, even though there may not be “outliers” in
the usual sense.

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