GUIDE FOR INSTRUCTORS AND OTHER READERS
The book assumes a basic understanding of the linear regression model with matrix algebra. It is written at the mathematical level of the firstyear economics Ph.D. sequence, comparable to W.H. Greene Econometric Analysis (2003).
While some of the material in this book is covered in a firstyear sequence,
most of the material in this book appears in second year econometrics Ph.D.
courses or in dataoriented microeconomics field courses such as labor economics,
public economics or industrial organization. This book is intended to be
used as both an econometrics text and as an adjunct for such field courses.
More generally, the book is intended to be useful as a reference work for
applied researchers in economics, in related social sciences such as sociology
and political science, and in epidemiology.
The models chapters have been written to be as selfcontained
as possible, to minimize the amount of background material in the methods
chapters that needs to be read. For the specific models presented in
parts four and five (chapters 1423) it will generally be sufficient to read
the relevant chapter in isolation, except that some command of the general
estimation results in chapter 5 and in some cases chapter 6 will be necessary.
Most chapters are structured to begin with a discussion and example that
is accessible to a wide audience.
For instructors using this book as a course text it is best to introduce
the basic nonlinear crosssection and linear panel data models as early
as possible, skipping many of the methods chapters. The most commonlyused
nonlinear crosssection models are presented in chapters 1416, and require
knowledge of maximum likelihood and least squares estimation, presented in
chapter five. Chapter twentyone on linear panel data models requires even
less preparation, essentially just chapter four.
Table 1.2 provides an outline for a onequarter secondyear graduate course
taught at the University of California  Davis, immediately following the
required firstyear statistics and econometrics sequence. A quarter provides
sufficient time to cover the basic results given in the first half of the
chapters in this outline. With additional time one can go into further detail
or cover a subset of chapters eleven to thirteen on computationallyintensive
estimation methods (simulationbased estimation, the bootstrap which is
also briefly presented in chapter seven and Bayesian methods); additional
crosssection models (durations and counts) presented in chapters seventeen
to twenty; and additional panel data models (linear model extensions and nonlinear
models) given in chapters twentytwo and twentythree.
Outline of a twentylecture tenweek course:
Lectures 
Chapter 
Topic 
13 
4 
Review of linear models and asymptotic theory 
47 
5 
Estimation: Mestimation, ML and NLS 
8 
10 
Estimation: Numerical Optimization

911 
14,15 
Models: Binary and multinomial 
1214 
16 
Models: Censored and Truncated 
15 
6 
Estimation: GMM 
16 
7 
Testing: Hypothesis Tests 
1719 
21 
Models: Basic Linear Panel 
20 
9 
Estimation: Semiparametric 
At Indiana University  Bloomington, a fifteenweek semester long field
course in microeconometrics is based on material in most of Parts 4 and
5 (chapters 1423). The prerequisite courses for this course cover
material similar to the material in Part 2 (chapters 410).