计量经济学导论 第4版pdf电子书版本下载

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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

1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis 12

Summary 16

Key Terms 17

Computer Exercises 17

PART 1 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 25

2.3 Properties of OLS on Any Sample of Data 34

2.4 Units of Measurement and Functional Form 39

2.5 Expected Values and Variances of the OLS Estimators 44

2.6 Regression through the Origin 56

Summary 57

Key Terms 58

Computer Exercises 59

Appendix 2A 61

Chapter 3 Multiple Regression Analysis:Estimation 63

3.1 Motivation for Multiple Regression 63

3.2 Mechanics and Interpretation of Ordinary Least Squares 68

3.3 The Expected Value of the OLS Estimators 80

3.4 The Variance of the OLS Estimators 90

3.5 Efficiency of OLS:The Gauss-Markov Theorem 99

Summary 100

Key Terms 101

Computer Exercises 102

Appendix 3A 105

Chapter 4 Multiple Regression Analysis:Inference 109

4.1 Sampling Distributions of the OLS Estimators 109

4.2 Testing Hypotheses about a Single Population Parameter:The t Test 112

4.3 Confidence Intervals 130

4.4 Testing Hypotheses about a Single Linear Combination of the Parameters 132

4.5 Testing Multiple Linear Restrictions:The F Test 135

4.6 Reporting Regression Results 146

Summary 148

Key Terms 150

Computer Exercises 151

Chapter 5 Multiple Regression Analysis:OLS Asymptotics 154

5.1 Consistency 154

5.2 Asymptotic Normality and Large Sample Inference 159

5.3 Asymptotic Efficiency of OLS 166

Summary 167

Key Terms 168

Computer Exercises 168

Appendix 5A 169

Chapter 6 Multiple Regression Analysis with Qualitative Information:Binary(or Dummy)Variables 171

6.1 Describing Qualitative Information 171

6.2 A Single Dummy Independent Variable 172

6.3 Using Dummy Variables for Multiple Categories 179

6.4 Interactions Involving Dummy Variables 184

6.5 A Binary Dependent Variable:The Linear Probability Model 192

6.6 More on Policy Analysis and Program Evaluation 197

Summary 200

Key Terms 201

Computer Exercises 201

Chapter 7 HeterOskedasticity 207

7.1 Consequences of Heteroskedasticity for OLS 207

7.2 Heteroskedasticity-Robust Inference after OLS Estimation 208

7.3 Testing for Heteroskedasticity 212

7.4 Weighted Least Squares Estimation 218

7.5 The Linear Probability Model Revisited 231

Summary 234

Key Terms 234

Computer Exercises 235

Chapter 8 More on Specification 238

8.1 Functional Form Misspecification 238

Summary 244

Key Terms 244

Computer Exercises 244

PART 2 Regression Analysis with Tine Series Data 245

Chapter 9 Basic Regression Analysis with Time Series Data 246

9.1 The Nature of Time Series Data 246

9.2 Examples of Time Series Regression Models 248

9.3 Finite Sample Properties of OLS under Classical Assumptions 251

9.4 Functional Form,Dummy Variables,and Index Numbers 259

9.5 Trends and Seasonality 266

Summary 276

Key Terrns 277

Computer Exercises 277

Chapter 10 Further Issues in Using OLS with Time Series Data 281

10.1 Stationary and Nonstationary Time Series 281

10.2 Asymptotic Properties of OLS 283

10.3 Using Highly Persistent Time Series in Regression Analysis 289

Summary 296

Key Terms 297

Computer Exercises 297

Chapter 11 Serial Correlation and Heteroskedasticity in Time Series Regressions 302

11.1 Properties of OLS with Serially Correlated Errors 302

11.2 Testing for Serial Correlation 306

11.3 Correcting for Serial Correlation with Strictly Exogenous Regressors 313

11.4 Differencing and Serial Correlation 320

11.5 Serial Correlation-Robust Inference after OLS 322

11.6 Heteroskedasticity in Time Series Regressions 326

Summary 331

Key Terms 331

Computer Exercises 332

PART 3 Advanced Topics 337

Chapter 12 Advanced Panel Data Methods 338

12.1 Fixed Effects Estimation 338

12.2 Random Effects Models 346

12.3 Applying Panel Data Methods to Other Data Structures 351

Summary 353

Key Terms 353

Computer Exercises 354

Appendix 12A 357

Chapter 13 Instrumental Variables Estimation and Two Stage Least Squares 360

13.1 Motivation：Omitted Variables in a Simple Regression Model 361

13.2 IV Estimation of the Multiple Regression Model 371

13.3 Two Stage Least Squares 375

13.4 IV Solutions to Errors-in-Variables Problems 379

13.5 Testing for Endogeneity and Testing Overidentifying Restrictions 381

13.6 2SLS with Heteroskedasticity 385

13.7 Applying 2SLS to Time Series Equations 385

Summary 388

Key Terms 388

Computer Exercises 388

Appendix 13A 392

Chapter 14 Limited Dependent Variable Models 395

14.1 Logit and Probit Models for Binary Response 396

14.2 The Tobit Model for Corner Solution Responses 408

Summary 416

Key Terms 417

Computer Exercises 417

Chapter 15 Advanced Time Series Topics 422

15.1 Infinite Distributed Lag Models 423

15.2 Testing for Unit Roots 429

15.3 Cointegration and Error Correction Models 435

15.4 Forecasting 441

Summary 450

Key Terms 451

Computer Exercises 451

Appendix A The NormaI and Related Distributions 455

Appendix B Answers to Chapter Questions 463

Appendix C Statistical Tables 470

References 477