杰弗里·M. 伍德里奇（Jeffrey M. Wooldridge），密歇根州立大学经济学教授，1991年以来一直在该校任教。1986—1991年伍德里奇博士曾任麻省理工学院经济学助教授。1982年他以计算机科学与经济学为主攻方向而获加州大学伯克利分校艺术学士学位，并于1986年于加州大学圣迭戈分校获经济学博士学位。伍德里奇博士曾在国际知名期刊发表学术论文二三十篇，参与过多种书籍的篇章写作。他的获奖项目包括： Alfred P.斯隆(Sloan)研究员基金，计量经济理论Multa Scripsicl奖金，应用计量经济学期刊的R.斯通(Stone)爵士奖，以及三次获MIT 当年研究生班优秀教师奖。他还是计量经济学期刊(Journal of Econometrics)的资深会员。
Chapter 1 The Nature of Econometrics and Economic Data 1
Part 1: Regression Analysis with Cross-Sectional Data 19
Chapter 2 The Simple Regression Model 20
Chapter 3 Multiple Regression Analysis: Estimation 66
Chapter 4 Multiple Regression Analysis: Inference 117
Chapter 5 Multiple Regression Analysis: OLS Asymptotics 163
Chapter 6 Multiple Regression Analysis: Further Issues 181
Chapter 7 Multiple Regression Analysis with Qualitative Information 220
Chapter 8 Heteroskedasticity 262
Chapter 9 More on Specification and Data Issues 294
Part 2: Regression Analysis with Time Series Data 333
Chapter 10 Basic Regression Analysis with Time Series Data 334
Chapter 11 Further Issues in Using OLS with Time Series Data 366
Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 394
Part 3: Advanced Topics 425
Chapter 13 Pooling Cross Sections across Time: Simple Panel Data Methods 426
Chapter 15 Instrumental Variables Estimation and Two-Stage Least Squares 495
Chapter 16 Simultaneous Equations Models 534
Chapter 19 Carrying Out an Empirical Project 642
aPPendices
Advanced Treatment E The Linear Regression Model in Matrix Form 760
Answers to Going Further Questions 775
References 791
Glossary 797
v
Contents
Preface xii
About the Author xix
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
1-3a Cross-Sectional Data 5
1-3b Time Series Data 7
1-3c Pooled Cross Sections 8
1-3d Panel or Longitudinal Data 9
1-3e A Comment on Data Structures 10
1-4 Causality， Ceteris Paribus， and Counterfactual Reasoning 10
Summary 14
Key Terms 15
Problems 15
Computer Exercises 15
Regression Analysis with Cross-Sectional Data 19
chapter 2 The Simple Regression Model 20
2-1 definition of the Simple Regression Model 20
2-2 deriving the ordinary Least Squares estimates 24
2-2a A Note on Terminology 31
2-3 Properties of oLS on Any Sample of data 32
2-3a Fitted Values and Residuals 32
2-3b Algebraic Properties of OLS Statistics 32
2-3c Goodness-of-Fit 35
2-4 Units of Measurement and Functional Form 36
2-4a The Effects of Changing Units of Measurement on OLS Statistics 36
2-4b Incorporating Nonlinearities in Simple Regression 37
2-4c The Meaning of “Linear” Regression 40
2-5 expected values and variances of the oLS estimators 40
2-5a Unbiasedness of OLS 40
2-5b Variances of the OLS Estimators 45
2-5c Estimating the Error Variance 48
2-6 Regression through the origin and Regression on a Constant 50
2-7 Regression on a Binary explanatory variable 51
2-7a Counterfactual Outcomes， Causality， and Policy Analysis 53
Summary 56
Key Terms 57
Problems 58
Computer Exercises 62
chapter 3 Multiple Regression Analysis: Estimation 66
3-1 Motivation for Multiple Regression 67
3-1a The Model with Two Independent Variables 67
3-1b The Model with k Independent Variables 69
3-2 Mechanics and interpretation of ordinary Least Squares 70
3-2a Obtaining the OLS Estimates 70
3-2b Interpreting the OLS Regression Equation 71
3-2c On the Meaning of “Holding Other Factors Fixed” in Multiple Regression 73
3-2d Changing More Than One Independent Variable Simultaneously 74
vi
3-2e OLS Fitted Values and Residuals 74
3-2f A “Partialling Out” Interpretation of Multiple Regression 75
3-2g Comparison of Simple and Multiple Regression Estimates 75
3-2h Goodness-of-Fit 76
3-2i Regression through the Origin 79
3-3 the expected value of the oLS estimators 79
3-3a Including Irrelevant Variables in a Regression Model 83
3-3b Omitted Variable Bias: The Simple Case 84
3-3c Omitted Variable Bias: More General Cases 87
3-4 the variance of the oLS estimators 87
3-4a The Components of the OLS Variances: Multicollinearity 89
3-4b Variances in Misspecified Models 92
3-4c Estimating s2: Standard Errors of the OLS Estimators 93
3-5 efficiency of oLS: the Gauss-Markov theorem 95
3-6 Some Comments on the Language of Multiple Regression Analysis 96
3-7 Several Scenarios for Applying Multiple Regression 97
3-7a Prediction 98
3-7b Efficient Markets 98
3-7c Measuring the Tradeoff between Two Variables 99
3-7d Testing for Ceteris Paribus Group Differences 99
3-7e Potential Outcomes， Treatment Effects， and Policy Analysis 100
Summary 102
Key Terms 104
Problems 104
Computer Exercises 109
chapter 4 Multiple Regression Analysis: Inference 117
4-1 Sampling distributions of the oLS estimators 117
4-2 testing hypotheses about a Single Population
4-2e A Reminder on the Language of Classical Hypothesis Testing 132
4-2f Economic， or Practical， versus Statistical Significance 132
4-3 Confidence intervals 134
4-4 testing hypotheses about a Single Linear Combination of the Parameters 136
4-5 testing Multiple Linear Restrictions: the
F test 139
4-5a Testing Exclusion Restrictions 139
4-5b Relationship between F and t Statistics 144 4-5c The R-Squared Form of the F Statistic 145 4-5d Computing p-Values for F Tests 146
4-5e The F Statistic for Overall Significance of a Regression 147
4-5f Testing General Linear Restrictions 148
4-6 Reporting Regression Results 149
4-7 Revisiting Causal effects and Policy Analysis 151
Summary 152
Key Terms 154
Problems 154
Computer Exercises 159
chapter 5 Multiple Regression Analysis: OLS Asymptotics 163
5-1 Consistency 164
5-1a Deriving the Inconsistency in OLS 167
5-2 Asymptotic normality and Large Sample inference 168
5-2a Other Large Sample Tests: The Lagrange Multiplier Statistic 172
5-3 Asymptotic efficiency of oLS 175
Summary 176
Key Terms 176
Problems 176
Computer Exercises 178
chapter 6 Multiple Regression Analysis: Further Issues 181
Parameter: the t test 120 6-1 effects of data Scaling on oLS Statistics 181
4-2a Testing against One-Sided Alternatives 122 6-1a Beta Coefficients 184
4-2b Two-Sided Alternatives 126
4-2c Testing Other Hypotheses about bj 128
4-2d Computing p-Values for t Tests 130 6-2 More on Functional Form 186
6-2a More on Using Logarithmic Functional Forms 186
6-2b Models with Quadratics 188
6-2c Models with Interaction Terms 192
6-2d Computing Average Partial Effects 194
6-3 More on Goodness-of-Fit and Selection of Regressors 195
6-3a Adjusted R-Squared 196
6-3b Using Adjusted R-Squared to Choose between Nonnested Models 197
6-3c Controlling for Too Many Factors in Regression Analysis 199
6-3d Adding Regressors to Reduce the Error Variance 200
6-4 Prediction and Residual Analysis 201 6.4a Confidence Intervals for Predictions 201 6-4b Residual Analysis 205
6-4c Predicting y When log(y) Is the Dependent Variable 205
6-4d Predicting y When the Dependent Variable Is log(y) 207
Summary 209
Key Terms 211
Problems 211
Computer Exercises 214
chapter 7 Multiple Regression Analysis with Qualitative Information 220
7-1 describing Qualitative information 221
7-2 A Single dummy independent variable 222
7-2a Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y) 226
7-3 Using dummy variables for Multiple Categories 228
7-3a Incorporating Ordinal Information by Using Dummy Variables 230
7-4 interactions involving dummy variables 232 7-4a Interactions among Dummy Variables 232 7-4b Allowing for Different Slopes 233
7-4c Testing for Differences in Regression Functions across Groups 237
7-5 A Binary dependent variable: the Linear Probability Model 239
7-6 More on Policy Analysis and Program evaluation 244
7-6a Program Evaluation and Unrestricted Regression Adjustment 245
8-1 Consequences of heteroskedasticity for oLS 262
8-2 heteroskedasticity-Robust inference after oLS estimation 263
8-2a Computing Heteroskedasticity-Robust LM
Tests 267
8-3 testing for heteroskedasticity 269
8-3a The White Test for Heteroskedasticity 271
8-4 Weighted Least Squares estimation 273
8-4a The Heteroskedasticity Is Known up to a Multiplicative Constant 273
8-4b The Heteroskedasticity Function
Must Be Estimated: Feasible GLS 278
8-4c What If the Assumed Heteroskedasticity Function Is Wrong? 281
8-4d Prediction and Prediction Intervals with Heteroskedasticity 283
8-5 the Linear Probability Model Revisited 284
Summary 286
Key Terms 287
Problems 287
Computer Exercises 290
chapter 9 More on Specification and Data Issues 294
9-1 Functional Form Misspecification 295
9-1a RESET as a General Test for Functional Form Misspecification 297
9-1b Tests against Nonnested Alternatives 298
9-2 Using Proxy variables for Unobserved explanatory variables 299
9-2a Using Lagged Dependent Variables as Proxy Variables 303
9-2b A Different Slant on Multiple Regression 304
9-2c Potential Outcomes and Proxy Variables 305
9-3 Models with Random Slopes 306
9-4 Properties of oLS under Measurement error 308
9-4a Measurement Error in the Dependent Variable 308
9-4b Measurement Error in an Explanatory Variable 310
9-5 Missing data， nonrandom Samples， and outlying observations 313
9-5a Missing Data 313
9-5b Nonrandom Samples 315
9-5c Outliers and Influential Observations 317
9-6 Least Absolute deviations estimation 321
Problems 361
Computer Exercises 363
chapter 11 Further Issues in Using OLS with Time Series Data 366
11-1 Stationary and Weakly dependent time Series 367
Summary 323 11-1a Stationary and Nonstationary Time Series 367
Key Terms 324 11-1b Weakly Dependent Time Series 368
Problems 324 11-2 Asymptotic Properties of oLS 370
Computer Exercises 328
Regression Analysis with Time Series Data 333
chapter 10 Basic Regression Analysis with Time Series Data 334
10-1 the nature of time Series data 334
10-2 examples of time Series Regression Models 335
10-2a Static Models 336
10-2b Finite Distributed Lag Models 336
10-2c A Convention about the Time Index 338
10-3 Finite Sample Properties of oLS under Classical Assumptions 339
10-3a Unbiasedness of OLS 339
10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem 342
10-3c Inference under the Classical Linear Model Assumptions 344
10-4 Functional Form， dummy variables， and index numbers 345
10-5 trends and Seasonality 351
10-5a Characterizing Trending Time Series 351
10-5b Using Trending Variables in Regression Analysis 354
10-5c A Detrending Interpretation of Regressions with a Time Trend 356
10-5d Computing R-Squared When the Dependent Variable Is Trending 357
10-5e Seasonality 358
Summary 360
Key Terms 361
11-3 Using highly Persistent time Series in Regression Analysis 376
11-3a Highly Persistent Time Series 376
11-3b Transformations on Highly Persistent Time Series 380
11-3c Deciding Whether a Time Series Is I(1) 381
11-4 dynamically Complete Models and the Absence of Serial Correlation 382
11-5 the homoskedasticity Assumption for time Series Models 385
Summary 386
Key Terms 387
Problems 387
Computer Exercises 390
chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 394
12-1 Properties of oLS with Serially Correlated errors 395
12-1a Unbiasedness and Consistency 395
12-1b Efficiency and Inference 395
12-1c Goodness-of-Fit 396
12-1d Serial Correlation in the Presence
of Lagged Dependent Variables 396
12-2 Serial Correlation–Robust inference after oLS 398
12-3 testing for Serial Correlation 401
12-3a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors 402
12-3b The Durbin-Watson Test under Classical Assumptions 403
12-3c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors 404
12-3d Testing for Higher-Order Serial Correlation 406
12-4 Correcting for Serial Correlation with Strictly exogenous Regressors 407
12-4a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model 408
12-4b Feasible GLS Estimation with AR(1) Errors 409
12-4c Comparing OLS and FGLS 411
12-4d Correcting for Higher-Order Serial Correlation 413
12-4e What if the Serial Correlation Model Is Wrong? 413
12-5 differencing and Serial Correlation 414
12-6 heteroskedasticity in time Series Regressions 415
12-6a Heteroskedasticity-Robust Statistics 416
12-6b Testing for Heteroskedasticity 416
12-6c Autoregressive Conditional Heteroskedasticity 417
12-6d Heteroskedasticity and Serial Correlation in Regression Models 418
Summary 419
Key Terms 420
Problems 420
Computer Exercises 421
Advanced Topics 425
chapter 13 Pooling Cross Sections across Time: Simple Panel Data Methods 426
13-1 Pooling independent Cross Sections across time 427
13-1a The Chow Test for Structural Change across
Summary 451
Key Terms 452
Problems 452
Computer Exercises 453
chapter 15 Instrumental Variables Estimation and Two-Stage Least Squares 495
15-1 Motivation: omitted variables in a Simple Regression Model 496
15-1a Statistical Inference with the IV Estimator 500
15-1b Properties of IV with a Poor Instrumental Variable 503
15-1c Computing R-Squared after IV Estimation 505
15-2 iv estimation of the Multiple Regression Model 505
15-3 two-Stage Least Squares 509
15-3a A Single Endogenous Explanatory Variable 509
15-3b Multicollinearity and 2SLS 511 15-3c Detecting Weak Instruments 512 15-3d Multiple Endogenous Explanatory
Variables 513
15-3e Testing Multiple Hypotheses after 2SLS Estimation 513
15-4 iv Solutions to errors-in-variables Problems 514
15-5 testing for endogeneity and testing overidentifying Restrictions 515
15-5a Testing for Endogeneity 515
15-5b Testing Overidentification Restrictions 516
15-6 2SLS with heteroskedasticity 518
15-7 Applying 2SLS to time Series equations 519
15-8 Applying 2SLS to Pooled Cross Sections and Panel data 521
Time 431 Summary 522
13-2 Policy Analysis with Pooled Cross Sections 431 Key Terms 523
13-2a Adding an Additional Control Group 436 Problems 523
13-2b A General Framework for Policy Analysis with Pooled Cross Sections 437
13-3 two-Period Panel data Analysis 439
13-3a Organizing Panel Data 444
13-4 Policy Analysis with two-Period Panel data 444
13-5 differencing with More than two time Periods 447
13-5a Potential Pitfalls in First Differencing Panel Data 451
16-1 the nature of Simultaneous equations Models 535
16-2 Simultaneity Bias in oLS 538
16-3 identifying and estimating a Structural equation 539
16-3a Identification in a Two-Equation System 540
16-3b Estimation by 2SLS 543
16-4 Systems with More than two equations 545
16-4a Identification in Systems with Three or More Equations 545
16-4b Estimation 546
16-5 Simultaneous equations Models with time Series 546
16-6 Simultaneous equations Models w
19-5d The Data 654
19-5e Results 655
19.5f Conclusions 656
19-5g Style Hints 656
Summary 658
Key Terms 658
Sample Empirical Projects 658 List of Journals 664
Computer Exercises 555
chapter 19 Carrying Out an Empirical Project 642
19-1 Posing a Question 642
19-2 Literature Review 644
19-3 data Collection 645
19-3a Deciding on the Appropriate Data Set 645
19-3b Entering and Storing Your Data 646
19-3c Inspecting， Cleaning， and Summarizing Your Data 647
19-4 econometric Analysis 648
19-5 Writing an empirical Paper 651
19-5a Introduction 651
19-5b Conceptual (or Theoretical) Framework 652
E-1 the Model and ordinary Least Squares estimation 760
E-1a The Frisch-Waugh Theorem 762
E-2 Finite Sample Properties of oLS 763
E-3 Statistical inference 767
E-4 Some Asymptotic Analysis 769
E-4a Wald Statistics for Testing Multiple Hypotheses 771
Summary 771
Key Terms 771
Problems 772
Answers to Going Further Questions 775 References 791
Glossary 797
內容試閱：
My motivation for writing the first edition of Introductory Econometrics: A Modern Approach was that I saw a fairly wide gap between how econometrics is taught to undergraduates and how empirical researchers think about and apply econometric methods. I became convinced that teaching introductory econometrics from the perspective of professional users of econometrics would actually simplify the presentation， in addition to making the subject much more interesting.
Based on the positive reactions to the several earlier editions， it appears that my hunch was correct. Many instructors， having a variety of backgrounds and interests and teaching students with different levels of preparation， have embraced the modern approach to econometrics espoused in this text. The emphasis in this edition is still on applying econometrics to real-world problems. Each econometric method is motivated by a particular issue facing researchers analyzing nonexperimental data. The focus in the main text is on understanding and interpreting the assumptions in light of actual empirical appli- cations: the mathematics required is no more than college algebra and basic probability and statistics.
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