计量经济学导论/高等学校经济学类英文版教材

本书从计量经济学的使用者的视角来讲授计量经济学的基础知识。全书按照所分析数据的类型不同而反计量经济学分为横截成数据篇和时间序例数据篇。本书的第一篇，便是在随机抽样的假定下，对横截面数据进行多元回归分析的问题。在第2章简要介绍简单回归模型之后，便直接开始进行多元回归分析。多元回归分析也是从估计和推断的基本程序出发，逐步过渡到对OLS的渐近性质、回归元的选择、定性因变量模型等专题的讨论，最后又对异方差性、模型误设和数据缺失等违背经典假定的极端情形进行了深入探讨，从而使学生能深刻理解在各种复杂的研究环境中如何利用多元回归分析技术。本书语言简明，计量理论与实际案例配合得当，非学适用于经济学、管理学、政治学、社会学等人文社会科学专业本科生一学期计量学课程教材。

杰弗瑞·M·伍德里奇(Jeffrey M．wooldridge)，1982年在加州大学伯克利分校获计算机科学与经济学学士学位，1986年在加州大学圣地亚哥分校获经济学博士学位。博士毕业后被麻省理工学院聘为经济学助教，5年间有3次获得MIT年度优秀研究生教师的荣誉，并获得斯隆研究奖及《计量经济理论》和《应用计量经济学》杂志颁发的优秀论文奖。自1991年受聘密歇根州立大学学校杰出教授以来，在计量经济学期刊上发表专业论文20多篇，出版两本颇有影响的教材(另一本是《横截面数据与综列数据的计量分析》)。

本书从计量经济学的使用者的视角来讲授计量经济学的基础知识，与传统教材不同的是按照所分析数据的类型不同而把计量经济学分为横截面数据篇和时间序列数据篇，让学生能尽早实际应用并研究问题，非常适用于经济学、管理学、政治学、社会学等人文社会学科专业本科生一学期计量经济学课程教材。

Chapter 1 The Nature of EconometriCS and Economic Data 1
1．1 What Is Econometrics? 1
1．2 Steps in Empirical Economic Analysis 2
1．3 The Structure of Economic Data 5
Cross—Sectional Data 6
Time SeriesData 8
Pooled Cross Sections 10
Panel or LongitudinoZ Data JD
A Comment on Data Structures i3
1．4 Causality and the Notion of CetefiS Paribus in Econometric
Analysis 13
Summary 18
Key TelTIIS 19
Chapter 2 The Simple Regression Model 22
2．1 Definition of the Simple Regression Model 22
2．2 Deriving the Ordinary Least Squares Estimates 27
A Note on Terminology 36
2_3 Mechanics Of oLS 36
Fitted Values and Residuals 36
Algebraic Properties of oLS Statistics 38
Goodness—of-Fit 40
2．4 Units Of Measurement and Functional Form 4 1
The Effects ofChanging Units ofMeasurement on oLs
Statistics 42
Incorporating Nonlinearities in Simple Regression 43
The Meaning of“Linear”Regression 46
2．5 Expected Values and Vances of the OLS Estimators 47
Unbiasedness of oLS 47
Variances ofthe 0Ls Estimators 53
· Estimating the Error VaHance 57
2．6 Regression Through the Origin 59
Summary 60
Key Terms 61
Problems 61
Computer Exercises 64
Appendix 2A 66
Chapter 3 Multiple Regression Analysis：Estimation 68
3．1 Motivation for Multiple Regression 68
e Modef wm0 Independent Variables 68
TheModelwfth kIndependent Variables 71
3．2 Mechanics and Interpretation of Ordinary Least Squares 73
Obtaining the oLs Estimates 73
Interpreting the oLS Regression Equation 74
On the Meaning of“Holding Other Factors Fixed”in Multiple
Regression 77
Changing More than One Independent Variable Simultaneously 77
oLs Fitted Values and Residuals 77
A“Partialling Out”Interpretation ofMultiple Regression 78
Comparison ofSimple and Multiple Regression Estimates 79
Goodness—of-Fit 80
Regression Through the Origin 83
3．3 The Expected Value of the OLS Estimators 84
Including Irrelevant Variables in a Regression Model 89
Omitted Variable BiaJ?The Simple Case 89
Omitted Variable Bins：More General Cases 93
3．4 The V{lriance of the OLS Estimators 95
The Components of the 0LS[riances：Multicollinearity 96
Variances fn Misspecified Mols 100
Estimating G2：Standard Errors ofthe oLs Estimators 101
3．5 Efficiency of OLS：The Gauss．Markov Theorem 103
Summary 104
KeyTerms 105
Problems 106
Computer Exercises 110
Appendix 3A 111
Chapter 4 Multiple Regression Analysis：Inference 1 1 6
4．1 Sampling Distributions of the OLS Estimators 11 6
4．2 Testing Hypotheses About a Single Population Parameter：
The t Test 】19
Testing Against One．Sided Alternatives 121
Tw0．Sided Alternatives 126
Testing Other Hypotheses About，128
ComputingP—Valuesfort Tests 131
A Reminder on the Language of Classical Hypothesis Testing 134
Economic，or Practical,versus Statistical Sign~ficance 134
4_3 Confidence Intervals 】37
4．4 Testing Hypotheses About a Single Linear Combination of the
Parameters 139
4．5 Testing Multiple Linear Restrictions：The F Test 142
Testing Exclusion Restrictions 142
PlationshBetween F and t Statistics 148
The R．Squared Form 0f the F Statistic 149
Computing P-Valuesfor F Tests 151
The F Statisticfor Overall Significance ofa Regression 152
Testing General Linear Restrictions 153
4．6 Reporting Regression Results 154
Summary 157
Key Terms 157
Problems 158
Computer Exercises 163
Chapter 5 Multiple Regression Analysis：0LS Asymptotics 1 66
5．1 Consistency 166
Deriving the Inconsistency in oLs 169
5．2 Asymptotic Normality and Large Sample Inference 17 1
Other Large Sample Tests：The Lagrange Multiplier
Statistic 175
5．3 Asymptotic Efficiency of OLS 177
Summary 179
KeyTerms 179
Problems 1 80
Computer Exercises 1 80
Appendix 5A 181
Chapter 6 Muttipte Regression Analysis：Further Issues 182
6．1 Effects of Data Scaling on OLS Stmisfics 182
Beta Coecients 185
6．2 More on Functional Form 187
More on Using Logarithmic Functional Forms 187
Models wfauadratics 189
ModelswithInraction Te"s 194
6．3 More on Goodness．of-Fit and Selection of Regressors 196
Adjusted R．Squared 197
Using Adjusted R-Squared to Choose Between Nonnes~d
Models 198
Controllingfor Too Many Factors in Regression Analysis 200
Adding Regressors to Reduce the Error Variance 202
6．4 Predicfion and Residual Analysis 202
ConfidenceIntervaIsforPcfions 203
Residual Analysis 206
Predicting Y when log(y)Is the Dependent Variable 207
Summary 210
Key TermS 211
Problems 211
Computer Exercises 213
Chapter 7 Multipie Regression Analysis with Qualitative Information：
． Binary(or Dummy)Variables 2 1 8
7．1 Describing Qualitative Information 2 1 8
7．2 A Single Dummy Independent Variable 220
Interpreting Coefficients onDummyExplanatory Variables
when the Dependent Variable Is log(y)225
7．3 Using Dummy Variables for Multiple Categories 227
Incorporating Ordinal Information by Using Dummy
VariabS 228
7．4 Interactions Involving Dummy Vables 232
nteractions Among Dummy Variables 232
AllowingforDifferentSlopes 233
Testing for Differences in Regression Functions Across
Groups 237
7．5 A Binary Dependent Variable：The Linear ProbabilitV Model 240
7．6 More on Policy Analysis and Program Evaluation 245
Summary 248
KeyTerms 249
Problems 249
Computer Exercises 252
Chapter 8 ．Heteroskedastieity 257
8．1 Consequences of Heteroskedasticity for 0LS 257
8．2 Heteroskedasticity．Robust Inference After OLS Estimation 258
Computing Heteroskedasticity．Robust LM Tests 262
8．3 Testing for Heteroskedasticity 264
The WrHeteroskedasticity 268
8．4 W．eighted Least Squares Estimation 270
The Heteroskedasticity Is Known“to a Multiplicative
Constant 270
The Heteroskedasticity Function Must Be Estimated?Feasible
GLS 276
8．5 T11e Linear Probability Model Revisited 280
Summary 283
KeyTerms 283
Problems 284
Computer Exercises 285
Chapter 9 More 011 Spe~ification and Data Problem$ 289
9．1 Functional Form Misspecification 289
RESET as a General Test for Functional Fonn
Misspec~fication 292
Tests Against Nonnested AIternatives 2
9．2 Using Proxy Variables for Unobserved Explanatory Variables 295
Using Lagged Dependent Variables as Proxy Variables 300
9．3 Properties Of OLS Under Measurement Error 302
Measurement ErrDr fn the DependPzriable 302
MeasurementErrorin anExplanatory Variable 305
9．4 Missing Data，Nonrandom Samples，and Outlying Observations 309
Missing Data 309
Nonrandom Samples 310
0utliers and Influentiaf Observations 312
Summary 317
Chapter 10 Basic Regression Analysis with Time Series Data 324
10．1 T

自教育部在《关于加强高等学校本科教学工作提高教学质量的若干意见》[教高(2001)4号]中提出双语教学的要求后，各地高校相继开设了一系列双语教学课程。这对提高学生的学科和外文水平，开阔国际视野，培养创新型人才起到了重要的作用；一大批教师也逐渐熟悉了外文授课，自身的教学水平和能力得到较大提高，具备国际学术思维的中青年教师脱颖而出。同时，经过近几年的双语教学实践，国外原版教材量大、逻辑不够清晰、疏离中国现实等问题也影响了双语教学的效果。因此，对外版教材进行本土化的精简改编，使之更加适合我国的双语教学已提上教材建设日程。 为了满足高等学校经济管理类双语课程本土化教学的..

Chapter 1 discusses the scope of econometriCS and raises general issues that result from the application of econometric methods．Section 1．3 examines the kinds of data sets that are used in business，economics，and other social sciences．Section1．4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences．1．1 WHAT IS ECONOMETRICS?Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program．Suppose this program teaches workers various ways to use computers in the manufacturing process．The twenty—week program offers courses during nonworking hours．Any hourly manufacturing worker may participate，and enrollment in all or part of the program is voluntary．You are to determine what．if any，effect the training program has on each worker’S subsequent hourly wage． Now,supposeyouworkforaninvestmentbank．Youareto studythe returnsondif-ferent investment strategies involving short—term U．S．treasury bills to decide whether they comply with implied economic theories． The task of answering such questions may seem daunting at first．At this point，you may only have a Vague idea of the kind of data you would need to collect．By the end of this introductory econometrics course，you should know how to use econo—metric methods to formally evaluate a job training program or to test a simple eco—nomic theory． EconometriCS is based upon the development of statistical methods for estimatingeconomic relationships，testing economic theories，and evaluating and implementinggovemment and business policy．The most common application of econometriCS iS theforecasting of such important macroeconomic variables as interest rates，inflation rates。and gross domestic product．While forecasts of economic indicators are highly visibleand often widely published，econometric methods Can be used in economic areas thathave nothing to do with macroeconomic forecast ……