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CONTENTS
PREFACE iii
SUGGESTED COURSE OUTLINES iv
Chapter 1 The Nature of Econometrics and Economic Data 1
Chapter 2 The Simple Regression Model 6
Chapter 12 Serial Correlation and Heteroskedasticity in 143
Time Series Regressions
Chapter 13 Pooling Cross Sections Across Time. Simple 156
Panel Data Methods
Chapter 14 Advanced Panel Data Methods 172
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Chapter 18 Advanced Time Series Topics 243
Chapter 19 Carrying Out an Empirical Project 259
Appendix A Basic Mathematical Tools 260
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This manual contains suggested course outlines, teaching notes, and detailed solutions to all of
the problems and computer exercises in Introductory Econometrics: A Modern Approach, 5e.
For several problems, I have added additional notes about interesting asides or suggestions for
how to modify or extend the problem.
I solved the computer exercises using various versions of Stata, starting with version 4.0 and
running through version 12.0. Nevertheless, almost all of the estimation methods covered in the
text have been standardized, and different econometrics or statistical packages should give the
same answers. There can be differences when applying more advanced techniques, as
conventions sometimes differ on how to choose or estimate auxiliary parameters. (Examples
include heteroskedasticity-robust standard errors, estimates of a random effects model, and
corrections for sample selection bias.)
The fifth edition of the text drops the chapter numbers preceding the problems and computer
exercises. I have kept the chapter numbers in the solutions manual so that it is easy to keep track
of where one is. For example, the solution to problem 4 in chapter 3 is labeled 3.4 and computer
exercise 6 in chapter 8 is labeled C8.6.
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SUGGESTED COURSE OUTLINES
For an introductory, one-semester course, I like to cover most of the material in Chapters 1
through 8 and Chapters 10 through 12, as well as parts of Chapter 9 (but mostly through
examples). I do not typically cover all sections or subsections within each chapter. Under the
chapter headings listed below, I provide some comments on the material I find most relevant for
a first-semester course.
I typically do not begin with a review of basic algebra, probability, and statistics. In my
experience, this takes too long and the payoff is minimal. (Students tend to think that they are
taking another statistics course and start to drift away from the material.) Instead, when I need a
tool (such as the summation or expectations operator), I briefly review the necessary definitions
and key properties. Statistical inference is not more difficult to describe in the context of multiple
regression than in testing about mean a mean from a population, and so I briefly review the
principles of statistical inference during multiple regression analysis. Appendices A, B, and C are
fairly extensive. When I cover asymptotic properties of OLS, I provide a brief discussion of the
main definitions and limit theorems. If students need more than the brief review provided in
class, I point them to the appendices.
(Chapters 13 and 14) emphasize how these data structures can be used, in conjunction with
econometric methods, for policy evaluation. Chapter 15, which introduces the method of
instrumental variables, is also important for policy analysis. Most modern IV applications are
used to address the problems of omitted variables (unobserved heterogeneity) or measurement
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error. I have intentionally separated out the conceptually more difficult topic of simultaneous
equations models in Chapter 16.
Chapter 17, in particular the material on probit, logit, Tobit, and Poisson regression models, is a
good introduction to nonlinear econometric methods. Specialized courses that emphasize
applications in labor economics can use the material on sample selection corrections. Duration
models are also briefly covered as an example of a censored regression model.