伍德里奇《现代入门经济计量学》第六版：实践导向的方法解析

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"《入门经济计量学：现代方法（第6版）》是由Jeffrey M. Wooldridge撰写的一本经济学教材，他来自密歇根州立大学。这本书以一种实践为导向的方式，引领读者了解当今经济研究者如何实际应用经济计量学方法。它与传统教材不同，不仅是一套抽象工具的集合，而是强调了其在商业决策、政策评估和预测中的实际效用。该书结构清晰，围绕分析数据类型进行组织，只在必要时引入假设，这使得学习过程更加直观，有助于培养出更优质的经济计量学实践。书中包含超过100个引人入胜的数据集，提供六种格式，以便于学生和研究人员进行实证分析。此外，每版都更新了最新的领域发展动态，确保内容与时俱进。第6版《入门经济计量学：现代方法》涵盖了经济计量学在现实世界中的广泛影响，深入剖析了如何利用统计方法解决实际问题，从宏观政策到微观决策，从金融市场到消费者行为，都能找到其身影。由于版权和电子权利的限制，部分第三方内容可能被隐去，但编审认为这些删减不影响整体的学习体验。出版商保留随时根据后续版权要求调整或删除内容的权利，但读者仍能通过访问www.cengage.com获取更多有价值的信息，包括定价、版本更新和替代格式。对于想要系统学习经济计量学并将其应用于实践的学生和专业人士来说，《入门经济计量学：现代方法（第6版）》是一本不可或缺的参考书籍，它提供了扎实的理论基础和丰富的实际案例，帮助读者掌握这一领域的核心技能。"

xiv Preface

After introducing Assumptions MLR.1 to MLR.3, one can discuss the algebraic properties of ordi-

nary least squares—that is, the properties of OLS for a particular set of data. By adding Assumption

MLR.4, we can show that OLS is unbiased (and consistent). Assumption MLR.5 (homoskedastic-

ity) is added for the Gauss-Markov Theorem and for the usual OLS variance formulas to be valid.

Assumption MLR.6 (normality), which is not introduced until Chapter 4, is added to round out the

classical linear model assumptions. The six assumptions are used to obtain exact statistical inference

and to conclude that the OLS estimators have the smallest variances among all unbiased estimators.

I use parallel approaches when I turn to the study of large-sample properties and when I treat

regression for time series data in Part 2. The careful presentation and discussion of assumptions

makes it relatively easy to transition to Part 3, which covers advanced topics that include using pooled

cross-sectional data, exploiting panel data structures, and applying instrumental variables methods.

Generally, I have strived to provide a unified view of econometrics, where all estimators and test sta-

tistics are obtained using just a few intuitively reasonable principles of estimation and testing (which,

of course, also have rigorous justification). For example, regression-based tests for heteroskedasticity

and serial correlation are easy for students to grasp because they already have a solid understanding

of regression. This is in contrast to treatments that give a set of disjointed recipes for outdated econo-

metric testing procedures.

Throughout the text, I emphasize ceteris paribus relationships, which is why, after one chapter on

the simple regression model, I move to multiple regression analysis. The multiple regression setting

motivates students to think about serious applications early. I also give prominence to policy analysis

with all kinds of data structures. Practical topics, such as using proxy variables to obtain ceteris pari-

bus effects and interpreting partial effects in models with interaction terms, are covered in a simple

fashion.

New to This Edition

I have added new exercises to almost every chapter, including the appendices. Most of the new com-

puter exercises use new data sets, including a data set on student performance and attending a Catholic

high school and a time series data set on presidential approval ratings and gasoline prices. I have also

added some harder problems that require derivations.

There are several changes to the text worth noting. Chapter 2 contains a more extensive dis-

cussion about the relationship between the simple regression coefficient and the correlation coef-

ficient. Chapter 3 clarifies issues with comparing R-squareds from models when data are missing

on some variables (thereby reducing sample sizes available for regressions with more explanatory

variables).

Chapter 6 introduces the notion of an average partial effect (APE) for models linear in the param-

eters but including nonlinear functions, primarily quadratics and interaction terms. The notion of an

APE, which was implicit in previous editions, has become an important concept in empirical work;

understanding how to compute and interpret APEs in the context of OLS is a valuable skill. For more

advanced classes, the introduction in Chapter 6 eases the way to the discussion of APEs in the non-

linear models studied in Chapter 17, which also includes an expanded discussion of APEs—including

now showing APEs in tables alongside coefficients in logit, probit, and Tobit applications.

In Chapter 8, I refine some of the discussion involving the issue of heteroskedasticity, including

an expanded discussion of Chow tests and a more precise description of weighted least squares when

the weights must be estimated. Chapter 9, which contains some optional, slightly more advanced

topics, defines terms that appear often in the large literature on missing data. A common practice

in empirical work is to create indicator variables for missing data, and to include them in a multiple

regression analysis. Chapter 9 discusses how this method can be implemented and when it will pro-

duce unbiased and consistent estimators.

Copyright 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).

Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

xvPreface

The treatment of unobserved effects panel data models in chapter 14 has been expanded to

include more of a discussion of unbalanced panel data sets, including how the fixed effects, random

effects, and correlated random effects approaches still can be applied. Another important addition is a

much more detailed discussion on applying fixed effects and random effects methods to cluster sam-

ples. I also include discussion of some subtle issues that can arise in using clustered standard errors

when the data have been obtained from a random sampling scheme.

Chapter 15 now has a more detailed discussion of the problem of weak instrumental variables so

that students can access the basics without having to track down more advanced sources.

Targeted at Undergraduates, Adaptable

for Master’s Students

The text is designed for undergraduate economics majors who have taken college algebra and one

semester of introductory probability and statistics. (Appendices A, B, and C contain the requisite

background material.) A one-semester or one-quarter econometrics course would not be expected

to cover all, or even any, of the more advanced material in Part 3. A typical introductory course

includes Chapters 1 through 8, which cover the basics of simple and multiple regression for

cross-sectional data. Provided the emphasis is on intuition and interpreting the empirical exam-

ples, the material from the first eight chapters should be accessible to undergraduates in most

economics departments. Most instructors will also want to cover at least parts of the chapters

on regression analysis with time series data, Chapters 10 and 12, in varying degrees of depth.

In the one-semester course that I teach at Michigan State, I cover Chapter 10 fairly carefully,

give an overview of the material in Chapter 11, and cover the material on serial correlation in

Chapter 12. I find that this basic one-semester course puts students on a solid footing to write

empirical papers, such as a term paper, a senior seminar paper, or a senior thesis. Chapter 9

contains more specialized topics that arise in analyzing cross-sectional data, including data

problems such as outliers and nonrandom sampling; for a one-semester course, it can be skipped

without loss of continuity.

The structure of the text makes it ideal for a course with a cross-sectional or policy analysis

focus: the time series chapters can be skipped in lieu of topics from Chapters 9 or 15. Chapter 13 is

advanced only in the sense that it treats two new data structures: independently pooled cross sections

and two-period panel data analysis. Such data structures are especially useful for policy analysis, and

the chapter provides several examples. Students with a good grasp of Chapters 1 through 8 will have

little difficulty with Chapter 13. Chapter 14 covers more advanced panel data methods and would

probably be covered only in a second course. A good way to end a course on cross-sectional methods

is to cover the rudiments of instrumental variables estimation in Chapter 15.

I have used selected material in Part 3, including Chapters 13 and 17, in a senior seminar geared

to producing a serious research paper. Along with the basic one-semester course, students who have

been exposed to basic panel data analysis, instrumental variables estimation, and limited dependent

variable models are in a position to read large segments of the applied social sciences literature.

Chapter 17 provides an introduction to the most common limited dependent variable models.

The text is also well suited for an introductory master’s level course, where the emphasis is on

applications rather than on derivations using matrix algebra. Several instructors have used the text to

teach policy analysis at the master’s level. For instructors wanting to present the material in matrix

form, Appendices D and E are self-contained treatments of the matrix algebra and the multiple regres-

sion model in matrix form.

At Michigan State, PhD students in many fields that require data analysis—including accounting,

agricultural economics, development economics, economics of education, finance, international eco-

nomics, labor economics, macroeconomics, political science, and public finance—have found the text

xvi Preface

to be a useful bridge between the empirical work that they read and the more theoretical econometrics

they learn at the PhD level.

Design Features

Numerous in-text questions are scattered throughout, with answers supplied in Appendix F. These

questions are intended to provide students with immediate feedback. Each chapter contains many

numbered examples. Several of these are case studies drawn from recently published papers, but

where I have used my judgment to simplify the analysis, hopefully without sacrificing the main point.

The end-of-chapter problems and computer exercises are heavily oriented toward empirical work,

rather than complicated derivations. The students are asked to reason carefully based on what they

have learned. The computer exercises often expand on the in-text examples. Several exercises use data

sets from published works or similar data sets that are motivated by published research in economics

and other fields.

A pioneering feature of this introductory econometrics text is the extensive glossary. The short

definitions and descriptions are a helpful refresher for students studying for exams or reading empiri-

cal research that uses econometric methods. I have added and updated several entries for the fifth

edition.

Data Sets—Available in Six Formats

This edition adds R data set as an additional format for viewing and analyzing data. In response to

popular demand, this edition also provides the Minitab

format. With more than 100 data sets in six

different formats, including Stata

, EViews

, Minitab

, Microsoft

Excel, and R, the instructor has

many options for problem sets, examples, and term projects. Because most of the data sets come from

actual research, some are very large. Except for partial lists of data sets to illustrate the various data

structures, the data sets are not reported in the text. This book is geared to a course where computer

work plays an integral role.

Updated Data Sets Handbook

An extensive data description manual is also available online. This manual contains a list of data

sources along with suggestions for ways to use the data sets that are not described in the text. This

unique handbook, created by author Jeffrey M. Wooldridge, lists the source of all data sets for quick

reference and how each might be used. Because the data book contains page numbers, it is easy to

see how the author used the data in the text. Students may want to view the descriptions of each data

set and it can help guide instructors in generating new homework exercises, exam problems, or term

projects. The author also provides suggestions on improving the data sets in this detailed resource that

is available on the book’s companion website at http://login.cengage.com and students can access it

free at www.cengagebrain.com.

Instructor Supplements

Instructor’s Manual with Solutions

The Instructor’s Manual with Solutions contains answers to all problems and exercises, as well as

teaching tips on how to present the material in each chapter. The instructor’s manual also contains

xviiPreface

sources for each of the data files, with many suggestions for how to use them on problem sets, exams,

and term papers. This supplement is available online only to instructors at http://login.cengage.com.

PowerPoint Slides

Exceptional PowerPoint

presentation slides help you create engaging, memorable lectures. You will

find teaching slides for each chapter in this edition, including the advanced chapters in Part 3. You can

modify or customize the slides for your specific course. PowerPoint

slides are available for conve-

nient download on the instructor-only, password-protected portion of the book’s companion website

at http://login.cengage.com.

Scientific Word Slides

Developed by the author, Scientific Word

slides offer an alternative format for instructors who

prefer the Scientific Word

platform, the word processor created by MacKichan Software, Inc. for

composing mathematical and technical documents using LaTeX typesetting. These slides are based

on the author’s actual lectures and are available in PDF and TeX formats for convenient download

on the instructor-only, password-protected section of the book’s companion website at http://login

.cengage.com.

Test Bank

Cengage Learning Testing, powered by Cognero

is a flexible, online system that allows you to

import, edit, and manipulate content from the text’s test bank or elsewhere. You have the flexibility

to include your own favorite test questions, create multiple test versions in an instant, and deliver

tests from your LMS, your classroom, or wherever you want. In the test bank for INTRODUCTORY

ECONOMETRICS, 6E you will find a wealth and variety of problems, ranging from multiple-choice

to questions that require simple statistical derivations to questions that require interpreting computer

output.

Student Supplements

MindTap

for INTRODUCTORY ECONOMETRICS, 6E provides you with the tools you need to

better manage your limited time—you can complete assignments whenever and wherever you are

ready to learn with course material specially customized by your instructor and streamlined in one

proven, easy-to-use interface. With an array of tools and apps—from note taking to flashcards—you

will get a true understanding of course concepts, helping you to achieve better grades and setting the

groundwork for your future courses.

Aplia

Millions of students use Aplia™ to better prepare for class and for their exams. Aplia assignments

mean “no surprises”—with an at-a-glance view of current assignments organized by due date. You

always know what’s due, and when. Aplia ties your lessons into real-world applications so you get a

bigger, better picture of how you’ll use your education in your future workplace. Automatic grading

and immediate feedback helps you master content the right way the first time.

xviii Preface

Student Solutions Manual

Now you can maximize your study time and further your course success with this dynamic online

resource. This helpful Solutions Manual includes detailed steps and solutions to odd-numbered prob-

lems as well as computer exercises in the text. This supplement is available as a free resource at

www.cengagebrain.com.

Suggestions for Designing Your Course

I have already commented on the contents of most of the chapters as well as possible outlines for

courses. Here I provide more specific comments about material in chapters that might be covered or

skipped:

Chapter 9 has some interesting examples (such as a wage regression that includes IQ score as

an explanatory variable). The rubric of proxy variables does not have to be formally introduced to

present these kinds of examples, and I typically do so when finishing up cross-sectional analysis. In

Chapter 12, for a one-semester course, I skip the material on serial correlation robust inference for

ordinary least squares as well as dynamic models of heteroskedasticity.

Even in a second course I tend to spend only a little time on Chapter 16, which covers simultane-

ous equations analysis. I have found that instructors differ widely in their opinions on the importance

of teaching simultaneous equations models to undergraduates. Some think this material is funda-

mental; others think it is rarely applicable. My own view is that simultaneous equations models are

overused (see Chapter 16 for a discussion). If one reads applications carefully, omitted variables and

measurement error are much more likely to be the reason one adopts instrumental variables estima-

tion, and this is why I use omitted variables to motivate instrumental variables estimation in Chapter

15. Still, simultaneous equations models are indispensable for estimating demand and supply func-

tions, and they apply in some other important cases as well.

Chapter 17 is the only chapter that considers models inherently nonlinear in their parameters,

and this puts an extra burden on the student. The first material one should cover in this chapter is on

probit and logit models for binary response. My presentation of Tobit models and censored regression

still appears to be novel in introductory texts. I explicitly recognize that the Tobit model is applied to

corner solution outcomes on random samples, while censored regression is applied when the data col-

lection process censors the dependent variable at essentially arbitrary thresholds.

Chapter 18 covers some recent important topics from time series econometrics, including test-

ing for unit roots and cointegration. I cover this material only in a second-semester course at either

the undergraduate or master’s level. A fairly detailed introduction to forecasting is also included in

Chapter 18.

Chapter 19, which would be added to the syllabus for a course that requires a term paper, is much

more extensive than similar chapters in other texts. It summarizes some of the methods appropriate

for various kinds of problems and data structures, points out potential pitfalls, explains in some detail

how to write a term paper in empirical economics, and includes suggestions for possible projects.

Acknowledgments

I would like to thank those who reviewed and provided helpful comments for this and previous

editions of the text:

Erica Johnson, Gonzaga University

Mary Ellen Benedict, Bowling Green

State University

Yan Li, Temple University

Melissa Tartari,

Yale University

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