计量经济学导论（2版英文改编，送网上教学资源）

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。

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》)。

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

书摘
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 mode
ls 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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