CHAPTER 14
TEACHING NOTES
My preference is to view the fixed and random effects methods of estimation as applying to the
same underlying unobserved effects model. The name unobserved effect is neutral to the issue
of whether the time-constant effects shou
CHAPTER 18
TEACHING NOTES
Several of the topics in this chapter, including testing for unit roots and cointegration, are now
staples of applied time series analysis. Instructors who like their course to be more time series
oriented might cover this chapte
CHAPTER 17
TEACHING NOTES
I emphasize to the students that, first and foremost, the reason we use the probit and logit models
is to obtain more reasonable functional forms for the response probability. Once we move to a
nonlinear model with a fully specif
CHAPTER 19
TEACHING NOTES
This is a chapter that students should read if you have assigned them a term paper. I used to
allow students to choose their own topics, but this is difficult in a first-semester course, and
places a heavy burden on instructors o
CHAPTER 16
TEACHING NOTES
I spend some time in Section 16.1 trying to distinguish between appropriate 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
CHAPTER 15
TEACHING NOTES
When I wrote the first edition, I took the novel approach of introducing instrumental variables as
a way of solving the omitted variable (or unobserved heterogeneity) problem. Traditionally, a
students first exposure to IV method
CHAPTER 12
TEACHING NOTES
Most of this chapter deals with serial correlation, but it also explicitly considers
heteroskedasticity in time series regressions. The first section allows a review of what
assumptions were needed to obtain both finite sample an
CHAPTER 13
TEACHING NOTES
While this chapter falls under Advanced Topics, most of this chapter requires no more
sophistication than the previous chapters. (In fact, I would argue that, with the possible
exception of Section 13.5, this material is easier t
CHAPTER 11
TEACHING NOTES
Much of the material in this chapter is usually postponed, or not covered at all, in an introductory
course. However, as Chapter 10 indicates, the set of time series applications that satisfy all of
the classical linear model ass
CHAPTER 8
TEACHING NOTES
This is a good place to remind students that homoskedasticity played no role in showing that
OLS is unbiased for the parameters in the regression equation. In addition, you probably should
mention that there is nothing wrong with
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
CHAPTER 7
TEACHING NOTES
This is a fairly standard chapter on using qualitative information in regression analysis, although
I try to emphasize examples with policy relevance (and only cross-sectional applications are
included.).
In allowing for different
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 tha
CHAPTER 6
TEACHING NOTES
I cover most of Chapter 6, but not all of the material in great detail. I use the example in Table
6.1 to quickly run through the effects of data scaling on the important OLS statistics. (Students
should already have a feel for th
CHAPTER 5
TEACHING NOTES
Chapter 5 is short, but it is conceptually more difficult than the earlier chapters, primarily
because it requires some knowledge of asymptotic properties of estimators. In class, I give a
brief, heuristic description of consisten
CHAPTER 2
TEACHING NOTES
This is the chapter where I expect students to follow most, if not all, of the algebraic derivations.
In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I
derive the variance. At a minimu
CHAPTER 3
TEACHING NOTES
For undergraduates, I do not work through most of the derivations in this chapter, at least not in
detail. Rather, I focus on interpreting the assumptions, which mostly concern the population.
Other than random sampling, the only
CHAPTER 4
TEACHING NOTES
At the start of this chapter is good time to remind students that a specific error distribution
played no role in the results of Chapter 3. That is because only the first two moments were
derived under the full set of Gauss-Markov
APPENDIX E
SOLUTIONS TO PROBLEMS
E.1 This follows directly from partitioned matrix multiplication in Appendix D. Write
x1
x2
2
X = , X = ( x1 x K x ), and y =
n
M
x
n
n
Therefore, X X = xxt and X y =
t
t =1
n
xy
t =1
t
t
y1
y2
M
y
n
. An equi
APPENDIX C
SOLUTIONS TO PROBLEMS
C.1 (i) This is just a special case of what we covered in the text, with n = 4: E( Y ) = and Var(
Y ) = 2/4.
(ii) E(W) = E(Y1)/8 + E(Y2)/8 + E(Y3)/4 + E(Y4)/2 = [(1/8) + (1/8) + (1/4) + (1/2)] = (1 +
1 + 2 + 4)/8 = , which
CHAPTER 1
TEACHING NOTES
You have substantial latitude about what to emphasize in Chapter 1. I find it useful to talk about
the economics of crime example (Example 1.1) and the wage example (Example 1.2) so that
students see, at the outset, that econometr
APPENDIX B
SOLUTIONS TO PROBLEMS
B.1 Before the student takes the SAT exam, we do not know nor can we predict with certainty
what the score will be. The actual score depends on numerous factors, many of which we
cannot even list, let alone know ahead of
APPENDIX D
SOLUTIONS TO PROBLEMS
0 1 6
2 1 7
20
D.1 (i) AB =
1 8 0 =
4 5 0 3 0 0 5
6 12
36 24
(ii) BA does not exist because B is 3 3 and A is 2 3.
D.2 This result is easy to visualize. If A and B are n n diagonal matrices, then AB is an n n
di
APPENDIX A
SOLUTIONS TO PROBLEMS
A.1 (i) $566.
(ii) The two middle numbers are 480 and 530; when these are averaged, we obtain 505, or
$505.
(iii) 5.66 and 5.05, respectively.
(iv) The average increases to $586 while the median is unchanged ($505).
A.2 (i