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。

　　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把计量经济学分为横截面数据篇和时间序列数据篇。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0Ls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，非常适用于经济学、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 The Namre of Time Series Data  324

10．2 Examples of Time Series Regression Models  326

StaticMols 326

Finite Distributed Lag Models 326

A Convention about the Time Index 329

10．3 Finite Sample Properties of OLS Under Classical Assumptions  329

Unbiasedness ofoLS 329

The Variances of the oLS Estimators and the Gauss．Markov

TheoFem 333

Inference under the ClassicaZ Linear ModeZ Assumptions 335

10．4 Functional Form．Dummy Vables．and Index Numbers  337

10．5 Tlrends and Seasonality  344

Characterizing Trending Time Series 344

Using Trending Variables in Regression Analysis 347

A Detrending Interpretation ofRegressions with a Time

Trend 350

Computing R—Squared when the Dependent Variable Is

Trending 351

Seasonality 353

Summary  355

KeyTerms  355

Problems  356

Computer Exercises  357

Chapter 1 l Further Issues in Using OLS with Time Series Data  3 60

11．1 Stationary and Weakly Dependent Time Series  360

Stationary and Nonstationaryme Series 361

Weakly Depencme Series 362

11．2 Asymptotic Properties of OLS  365

11．3 Using Highly Persistent Time Series in Regression Analysis  372

Highly Persistent Time Series 372

Transformations onHighlyPersistentTimeSeries 377

Deciding Wther ame Series Is／(1 J 378

11．4 Dynamically Complete Models and the Absence of Serial

Correlation  380

11．5 The Homoskedasticity Assumption for Time Series Models  382

Summary  383

Key Terms  384

·Problems  384

Compu~r Exercises  387

Key Terms  317

Problems  318

Computer Exercises  320

Chapter 1 2 Seriat Correlation and Heteroskedasticity in Time

Series Regressions  391

12．1 Properties of 0LS with Serially Correlated Errors  391

Unbiasedness and Consistency 391

Efficiency andInference 392

Goodness．of-Fit 393

Serial Correlation in the Presence ofLagged Dependent  ．

Variables 394

12．2 Testing for Serial Correlation  395

A t Testfor AR(1)Serial Correlation with Strictly Exogenous

Regressors 395

The Durbin一tson Test under ClassicaZ Assumptions 397

Testing for AJ J Seriaf Correlation without Strictly Exogenous

Regressors 399

Testing for Higher Order Seriaf Correlation 400

12．3 Correcting for Seri~Correlation with Strictly Exogenous

Regressors 402

Obtaining the Best Linear Unbiased Estimator in the AR(1)

Model 402

Feasible GLS Estimation with AR(1)Ermrs 404

Comparing 0LS and FGLs 406

Correcting for Higher 0rder Serf Correlation 408

12．4 Differencing and Serial Correlation 409

12．5 Seri~Correlation．Robust Inference After OLS 410

12．6 Heteroskedasticity in Time Series Regressions 414

Heteroskedasticity—Robust Statistics 414

Testing for Heteroskedasticity 414

Autoregressive Conc~tionaf Heteroskedasticity 416

Heteroskedasticity and Serial Correlation fn Regression

Models 418

Summary 419

KeyTerms 420

Problems 420

Computer Exercises 42 1

Appendix A Answers to Chapter Questions  39 1

Appendix B Statistical Tables  398

Glossary  G_1

  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 forecasting．For example，we will study the effects of political campaign expenditures on voting outcomes．We will consider the effect of school spending on student performance in the field of education．In addition．we willlearn how to use econometric methods for forecasting economic time series． Econometrics has evolved as a separate discipline from mathematical statistics because the former focuses on the problems inherent in collecting and analyzing nonex—perimental economic data．Nonexperimental data are not accumulated through con~oHed experiments on individuals，firms，or segments of the economy．(Nonexperimental data are sometimes called observational data to emphasize the fact that the researcher isa passive collector of the data．1 Experimental data are often collected in laboratory envi—ronments in the natural sciences，but they are much more difficult to obtain in the socialsciences．ile some social experiments can be devised，it is often impossible，prohibi-tively expensive，or morally repugnant to conduct the kinds of controlled experiments that would be needed to address economic issues．We give some specific examples of the dif-ferences between experimental and nonexperimental data in Section 1．4． Naturally。econometricians have borrowed from mathematical statisticians when—ever possible．The method of multiple regression analysis is the mainstay in both fields，but its focus and interpretation can differ markedly．In addition，economists havedevised new techniques to deal with the complexities of economic data and to test thepredictions of economic theories．1．2 STEPS IN EMPIRICAL ECONOMIC ANAI-YSiSEconometric methods are relevant in virtually every branch of applied economics．Theycome into play either when we have an economic theory to test or when we have a rela—tionship in mind that has some importance for business decisions or policy analysis．An empirical analysis uses data to test a theory or to estimate a relationship． How does one go about structuring an empirical economic analysis?Itmay seem obvi—OUS．but it is worth emphasizing that the first step in any empirical analysis is the carefulformulation of the question of interest．The question might deal with testing a certain aspect of an economic theory,or it might pertain to testing the ef_fects of a government policy．Inprinciple，econometric methods can be used to answer a wide range of questions． In some cases，especially those that involve the testing of economic theories，a for-mal economic model is constructed．An economic model consists of mathematical equations that describe various relationships．Economists are well-known for theirbuilding of models to describe a vast array of behaviors．For example．in intermediate microeconomics，individual consumption decisions，subject to a budget constraint，are described by mathematical models．The basic premise underlying these models is util-fty maximization．The assumption that individuals make choices to maximize their well-being，subject to resource constraints，gives us a very powerful framework for creatingtractable economic models and making clear predictions．In the context of consumption decisions，utility maximization leads to a set of demand equations．In a demand equa—tion，the quantity demanded of each commodity depends on the price of the goods，the price of substitute and complementary goods，the consumer’s income，and the individ—ual’s characteristics that affect taste．These equations can form the basis of an econo—metric analysis of consumer demand． Economists have used basic economic tools，such as the utility maximization frame—work，to explain behaviors that at first glance may appear to be noneconomic in nature．A classic example is Becker’s(1968)economic model of criminal behavior．    

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　　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需要，在教育部高等教育司的指导和支持下，高等教育出版社同Thomson Learning~国外著名出版公司通力合作，在国内首次推出了金融、会计、经济学等专业的英文原版改编教材。本套教材的遴选、改编和出版严格遵循了以下几个原则：

1．择优选取权威的新版本。在各专业选书论证会上，我们要求入选改编的教材不仅是在国际上多次再版的经典之作的最新版本，而且是近年来已在国内被试用的优秀教材。

2．改编后的教材力求内容规范简明，逻辑更加清晰，语言原汁原味，适合中国的双语教学。选择的改编人既熟悉原版教材内容又具有本书或本门课程双语教学的经验；在改编过程中，高等教育出版社组织了知名专家学者召开了数次改编和审稿会议，改编稿征求了众多教师的意见。

3．改编后的教材配有较丰富的辅助教学支持资源，教师可在网上免费获取。同时，改编后的教材厚度适中，定价标准较低。

由于原作者所处国家的政治、经济和文化背景等与我国不同，对书中所持观点，敬请广大读者在阅读过程中注意加以分析和鉴别。

此次英文改编教材的出版，得到了很多专家学者的支持和帮助，在此深表谢意!我们期待这批英文改编教材的出版能对我国经济管理类专业的教学能有所帮助，欢迎广大读者给我们提出宝贵的意见和建议。