Introductory econometrics A modern approach Seventh edition2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

Introductory econometrics A modern approach Seventh editionPDF格式电子书版下载

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CHAPTER1 The Nature of Econometrics and Economic Data1

1-1 What Is Econometrics？1

1-2 Steps in Empirical Economic Analysis2

1-3 The Structure of Economic Data5

1-3a Cross-Sectional Data5

1-3b Time Series Data7

1-3c Pooled Cross Sections8

1-3d Panel or Longitudinal Data9

1-3e A Comment on Data Structures10

1-4 Causality，Ceteris Paribus，and Counterfactual Reasoning10

Summary14

Key Terms15

Problems15

Computer Exercises15

PART1 Regression Analysis with Cross-Sectional Data19

CHAPTER 2 The Simple Regression Model20

2-1 Definition of the Simple Regression Model20

2-2 Deriving the Ordinary Least Squares Estimates24

2-2a A Note on Terminology31

2-3 Properties of OLS on Any Sample of Data32

2-3a Fitted Values and Residuals32

2-3b Algebraic Properties of OLS Statistics32

2-3c Goodness-of-Fit35

2-4 Units of Measurement and Functional Form36

2-4a The Effects of Changing Units of Measurement on OLS Statistics36

2-4b Incorporating Nonlinearities in Simple Regression37

2-4c The Meaning of “Linear” Regression40

2-5 Expected Values and Variances of the OLS Estimators40

2-5a Unbiasedness of OLS40

2-5b Variances of the OLS Estimators45

2-5c Estimating the Error Variance48

2-6 Regression through the Origin and Regression on a Constant50

2-7 Regression on a Binary Explanatory Variable51

2-7a Counterfactual Outcomes，Causality，and Policy Analysis53

Summary56

Key Terms57

Problems58

Computer Exercises62

CHAPTER 3 Multiple Regression Analysis：Estimation66

3-1 Motivation for Multiple Regression67

3-1a The Model with Two Independent Variables67

3-1 b The Model with k Independent Variables69

3-2 Mechanics and Interpretation of Ordinary Least Squares70

3-2a Obtaining the OLS Estimates70

3-2b Interpreting the OLS Regression Equation71

3-2c On the Meaning of “Holding Other Factors Fixed”in Multiple Regression73

3-2d Changing More Than One Independent Variable Simultaneously74

3-2e OLS Fitted Values and Residuals74

3-2f A “Partialling Out” Interpretation of Multiple Regression75

3-2g Comparison of Simple and Multiple Regression Estimates75

3-2h Goodness-of-Fit76

3-2i Regression through the Origin79

3-3 The Expected Value of the OLS Estimators79

3-3a Including Irrelevant Variables in a Regression Model83

3-3b Omitted Variable Bias：The Simple Case84

3-3c Omitted Variable Bias：More General Cases87

3-4 The Variance of the OLS Estimators87

3-4a The Components of the OLS Variances：Multicollinearity89

3-4b Variances in Misspecified Models92

3-4c Estimating σ2：Standard Errors of the OLS Estimators93

3-5 Efficiency of OLS：The Gauss-Markov Theorem95

3-6 Some Comments on the Language of Multiple Regression Analysis96

3-7 Several Scenarios for Applying Multiple Regression97

3-7a Prediction98

3-7b Efficient Markets98

3-7c Measuring the Tradeoff between Two Variables99

3-7d Testing for Ceteris Paribus Group Differences99

3-7e Potential Outcomes，Treatment Effects，and Policy Analysis100

Summary102

Key Terms104

Problems104

Computer Exercises109

CHAPTER 4 Multiple Regression Analysis：Inference117

4-1 Sampling Distributions of the OLS Estimators117

4-2 Testing Hypotheses about a Single Population Parameter：The tTest120

4-2a Testing against One-Sided Alternatives122

4-2b Two-Sided Alternatives126

4-2c Testing Other Hypotheses about β j128

4-2d Computing p-Values fort Tests130

4-2e A Reminder on the Language of Classical Hypothesis Testing132

4-2f Economic，or Practical，versus Statistical Significance132

4-3 Confidence Intervals134

4-4 Testing Hypotheses about a Single Linear Combination of the Parameters136

4-5 Testing Multiple Linear Restrictions：The F Test139

4-5a Testing Exclusion Restrictions139

4-5b Relationship between F and t Statistics144

4-5c The R-Squared Form of the F Statistic145

4-5d Computing p- Values for F Tests146

4-5e The F Statistic for Overall Significance of a Regression147

4-5f Testing General Linear Restrictions148

4-6 Reporting Regression Results149

4-7 Revisiting Causal Effects and Policy Analysis151

Summary152

Key Terms154

Problems154

Computer Exercises159

CHAPTER 5 Multiple Regression Analysis：OLS Asymptotics163

5-1 Consistency164

5-1a Deriving the Inconsistency in OLS167

5-2 Asymptotic Normality and Large Sample Inference168

5-2a Other Large Sample Tests：The Lagrange Multiplier Statistic172

5-3 Asymptotic Efficiency of OLS175

Summary176

Key Terms176

Problems176

Computer Exercises178

CHAPTER 6 Multiple Regression Analysis：Further Issues181

6-1 Effects of Data Scaling on OLS Statistics181

6-1a Beta Coefficients184

6-2 More on Functional Form186

6-2a More on Using Logarithmic Functional Forms186

6-2b Models with Quadratics188

6-2c Models with Interaction Terms192

6-2d Computing Average Partial Effects194

6-3 More on Goodness-of-Fit and Selection of Regressors195

6-3a Adjusted R-Squared196

6-3b Using Adjusted R-Squared to Choose between Nonnested Models197

6-3c Controlling for Too Many Factors in Regression Analysis199

6-3d Adding Regressors to Reduce the Error Variance200

6-4 Prediction and Residual Analysis201

6.4a Confidence Intervals for Predictions201

6-4b Residual Analysis205

6-4c Predicting y When log（y） Is the Dependent Variable205

6-4d Predicting y When the Dependent Variable Is log（y）207

Summary209

Key Terms211

Problems211

Computer Exercises214

CHAPTER 7 Multiple Regression Analysis with Qualitative Information220

7-1 Describing Qualitative Information221

7-2 A Single Dummy Independent Variable222

7-2a Interpreting Coeffcients on Dummy Explanatory Variables When the Dependent Variable Is log（y）226

7-3 Using Dummy Variables for Multiple Categories228

7-3a Incorporating Ordinal Information by Using Dummy Variables230

7-4 Interactions Involving Dummy Variables232

7-4a Interactions among Dummy Variables232

7-4b Allowing for Different Slopes233

7-4c Testing for Differences in Regression Functions across Groups237

7-5 A Binary Dependent Variable：The Linear Probability Model239

7-6 More on Policy Analysis and Program Evaluation244

7-6a Program Evaluation and Unrestricted Regression Adjustment245

7-7 Interpreting Regression Results with Discrete Dependent Variables249

Summary250

Key Terms251

Problems251

Computer Exercises256

CHAPTER 8 Heteroskedasticity262

8-1 Consequences of Heteroskedasticity for OLS262

8-2 Heteroskedasticity-Robust Inference after OLS Estimation263

8-2a Computing Heteroskedasticity-Robust LM Tests267

8-3 Testing for Heteroskedasticity269

8-3a The White Test for Heteroskedasticity271

8-4 Weighted Least Squares Estimation273

8-4a The Heteroskedasticity Is Known up to a Multiplicative Constant273

8-4b The Heteroskedasticity Function Must Be Estimated：Feasible GLS278

8-4c What If the Assumed Heteroskedasticity Function Is Wrong？281

8-4d Prediction and Prediction Intervals with Heteroskedasticity283

8-5 The Linear Probability Model Revisited284

Summary286

Key Terms287

Problems287

Computer Exercises290

CHAPTER 9 More on Specification and Data Issues294

9-1 Functional Form Misspecification295

9-1a RESET as a General Test for Functional Form Misspecification297

9-1b Tests against Nonnested Alternatives298

9-2 Using Proxy Variables for Unobserved Explanatory Variables299

9-2a Using Lagged Dependent Variables as Proxy Variables303

9-2b A Different Slant on Multiple Regression304

9-2c Potential Outcomes and Proxy Variables305

9-3 Models with Random Slopes306

9-4 Properties of OLS under Measurement Error308

9-4a Measurement Error in the Dependent Variable308

9-4b Measurement Error in an Explanatory Variable310

9-5 Missing Data，Nonrandom Samples，and Outlying Observations313

9-5a Missing Data313

9-5b Nonrandom Samples315

9-5c Outliers and Influential Observations317

9-6 Least Absolute Deviations Estimation321

Summary323

Key Terms324

Problems324

Computer Exercises328

PART2 Regression Analysis with Time Series Data333

CHAPTER 10 Basic Regression Analysis with Time Series Data334

10-1 The Nature of Time Series Data334

10-2 Examples of Time Series Regression Models335

10-2a Static Models336

10-2b Finite Distributed Lag Models336

10-2c A Convention about the Time Index338

10-3 Finite Sample Properties of OLS under Classical Assumptions339

10-3a Unbiasedness of OLS339

10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem342

10-3c Inference under the Classical Linear Model Assumptions344

10-4 Functional Form，Dummy Variables，and Index Numbers345

10-5 Trends and Seasonality351

10-5a Characterizing Trending Time Series351

10-5b Using Trending Variables in Regression Analysis354

10-5c A Detrending Interpretation of Regressions with a Time Trend356

10-5d Computing R-Squared When the Dependent Variable Is Trending357

10-5e Seasonality358

Summary360

Key Terms361

Problems361

Computer Exercises363

CHAPTER 11 Further Issues in Using OLS with Time Series Data366

11-1 Stationary and Weakly Dependent Time Series367

11-1a Stationary and Nonstationary Time Series367

11-1b Weakly Dependent Time Series368

11-2 Asymptotic Properties of OLS370

11-3 Using Highly Persistent Time Series in Regression Analysis376

11-3a Highly Persistent Time Series376

11-3b Transformations on Highly Persistent Time Series380

11-3c Deciding Whether a Time Series Is Ⅰ（1）381

11-4 Dynamically Complete Models and the Absence of Serial Correlation382

11-5 The Homoskedasticity Assumption for Time Series Models385

Summary386

Key Terms387

Problems387

Computer Exercises390

CHAPTER 12 Serial Correlation and Heteroskedasticity in Time Series Regressions394

12-1 Properties of OLS with Serially Correlated Errors395

12-1a Unbiasedness and Consistency395

12-1b Efficiency and Inference395

12-1c Goodness-of-Fit396

12-1d Serial Correlation in the Presence of Lagged Dependent Variables396

12-2 Serial Correlation-Robust Inference after OLS398

12-3 Testing for Serial Correlation401

12-3a A t Test forAR（1） Serial Correlation with Strictly Exogenous Regressors402

12-3b The Durbin-Watson Test under Classical Assumptions403

12-3c Testing for AR（1） Serial Correlation without Strictly Exogenous Regressors404

12-3d Testingfor Higher-Order Serial Correlation406

12-4 Correcting for Serial Correlation with Strictly Exogenous Regressors407

12-4a Obtaining the Best Linear Unbiased Estimator in the AR（1） Model408

12-4b Feasible GLS Estimation with AR（1）Errors409

12-4c Comparing OLS and FGLS411

12-4d Correcting for Higher-Order Serial Correlation413

12-4e What if the Serial Correlation Model Is Wrong？413

12-5 Differencing and Serial Correlation414

12-6 Heteroskedasticity in Time Series Regressions415

12-6a Heteroskedasticity-Robust Statistics416

12-6b Testing for Heteroskedasticity416

12-6c Autoregressive Conditional Heteroskedasticity417

12-6d Heteroskedasticity and Serial Correlation in Regression Models418

Summary419

Key Terms420

Problems420

Computer Exercises421

PART3 Advanced Topics425

CHAPTER 13 Pooling Cross Sections across Time：Simple Panel Data Methods426

13-1 Pooling Independent Cross Sections across Time427

13-1 a The Chow Test for Structural Change across Time431

13-2 Policy Analysis with Pooled Cross Sections431

13-2a Adding an Additional Control Group436

13-2b A General FrameworkforPolicy Analysis with Pooled Cross Sections437

13-3 Two-Period Panel Data Analysis439

13-3a Organizing Panel Data444

13-4 Policy Analysis with Two-Period Panel Data444

13-5 Differencing with More Than Two Time Periods447

13-5a Potential Pitfalls in First Differencing Panel Data451

Summary451

Key Terms452

Problems452

Computer Exercises453

CHAPTER 14 Advanced Panel Data Methods462

14-1 Fixed Effects Estimation463

14-1a The Dummy Variable Regression466

14-1b Fixed Effects or First Differencing？467

14-1c Fixed Effects with Unbalanced Panels468

14-2 Random Effects Models469

14-2a Random Effects or Pooled OLS？473

14-2b Random Effects or Fixed Effects？473

14-3 The Correlated Random Effects Approach474

14-3a Unbalanced Panels476

14-4 General Policy Analysis with Panel Data477

14-4a Advanced Considerations with Policy Analysis478

14-5 Applying Panel Data Methods to Other Data Structures480

Summary483

Key Terms484

Problems484

Computer Exercises486

CHAPTER 15 Instrumental Variables Estimation and Two-Stage Least Squares495

15-1 Motivation：Omitted Variables in a Simple Regression Model496

15-1 a Statistical Inference with the Ⅳ Estimator500

15-1 b Properties of Ⅳ with a Poor Instrumental Variable503

15-1 c Computing R -Squared after Ⅳ Estimation505

15-2 Ⅳ Estimation of the Multiple Regression Model505

15-3 Two-Stage Least Squares509

15-3a A Single Endogenous Explanatory Variable509

15-3b Multicollinearity and 2SLS511

15-3c Detecting Weak Instruments512

15-3d Multiple Endogenous Explanatory Variables513

15-3e Testing Multiple Hypotheses after 2SLS Estimation513

15-4 Ⅳ Solutions to Errors-in-Variables Problems514

15-5 Testing for Endogeneity and Testing Overidentifying Restrictions515

15-5a Testing for Endogeneity515

15-5b Testing Overidentification Restrictions516

15-6 2SLS with Heteroskedasticity518

15-7 Applying 2SLS to Time Series Equations519

15-8 Applying 2SLS to Pooled Cross Sections and Panel Data521

Summary522

Key Terms523

Problems523

Computer Exercises526

CHAPTER 16 Simultaneous Equations Models534

16-1 The Nature of Simultaneous Equations Models535

16-2 Simultaneity Bias in OLS538

16-3 Identifying and Estimating a Structural Equation539

16-3a Identification in a Two-Equation System540

16-3b Estimation by 2SLS543

16-4 Systems with More Than Two Equations545

16-4a Identification in Systems with Three or More Equations545

16-4b Estimation546

16-5 Simultaneous Equations Models with Time Series546

16-6 Simultaneous Equations Models with Panel Data549

Summary551

Key Terms552

Problems552

Computer Exercises555

CHAPTER 17 Limited Dependent Variable Models and Sample Selection Corrections559

17-1 Logit and Probit Models for Binary Response560

17-1a Specifying Logit and Probit Models560

17-1 b Maximum Likelihood Estimation of Logit and Probit Models563

17-1c Testing Multiple Hypotheses564

17-1d Interpreting the Logit and Probit Estimates565

17-2 The Tobit Model for Corner Solution Responses571

17-2a Interpreting the Tobit Estimates572

17-2b Specification Issues in Tobit Models578

17-3 The Poisson Regression Model578

17-4 Censored and Truncated Regression Models582

17-4a Censored Regression Models583

17-4b Truncated Regression Models586

17-5 Sample Selection Corrections588

17-5a When Is OLS on the Selected Sample Consistent？588

17-5b Incidental Truncation589

Summary593

Key Terms593

Problems594

Computer Exercises596

CHAPTER 18 Advanced Time Series Topics604

18-1 Infinite Distributed Lag Models605

18-1 a The Geometric （or Koyck） Distributed Lag Model607

18-1 b Rational Distributed Lag Models608

18-2 Testing for Unit Roots610

18-3 Spurious Regression614

18-4 Cointegration and Error Correction Models616

18-4a Cointegration616

18-4b Error Correction Models620

18-5 Forecasting622

18-5a Types of Regression Models Used for Forecasting623

18-5b One-Step-Ahead Forecasting624

18-5c Comparing One-Step-Ahead Forecasts627

18-5d Multiple-Step-Ahead Forecasts628

18-5e Forecasting Trending，Seasonal，and Integrated Processes631

Summary635

Key Terms636

Problems636

Computer Exercises638

CHAPTER 19 Carrying Out an Empirical Project642

19-1 Posing a Question642

19-2 Literature Review644

19-3 Data Collection645

19-3a Deciding on the Appropriate Data Set645

19-3b Entering and Storing Your Data646

19-3c Inspecting，Cleaning，and Summarizing Your Data647

19-4 Econometric Analysis648

19-5 Writing an Empirical Paper651

19-5a Introduction651

19-5b Conceptual （or Theoretical） Framework652

19-5c Econometric Models and Estimation Methods652

19-5d The Data654

19-5e Results655

19.5f Conclusions656

19-5g Style Hints656

Summary658

Key Terms658

Sample Empirical Projects658

List of Journals664

Data Sources665

MATH REFRESHER A Basic Mathematical Tools666

A-1 The Summation Operator and Descriptive Statistics666

A-2 Properties of Linear Functions668

A-3 Proportions and Percentages671

A-4 Some Special Functions and Their Properties672

A-4a Quadratic Functions672

A-4b The Natural Logarithm674

A-4c The Exponential Function677

A-5 Differential Calculus678

Summary680

Key Terms681

Problems681

MATH REFRESHER B Fundamentals of Probability684

B-1 Random Variables and Their Probability Distributions684

B-1 a Discrete Random Variables685

B-1b Continuous Random Variables687

B-2 Joint Distributions，Conditional Distributions，and Independence688

B-2a Joint Distributions and Independence688

B-2b Conditional Distributions690

B-3 Features of Probability Distributions691

B-3a A Measure of Central Tendency：The Expected Value691

B-3b Properties of Expected Values692

B-3c Another Measure ofCentral Tendency：The Median694

B-3d Measures of Variability：Variance and Standard Deviation695

B-3e Variance695

B-3f Standard Deviation696

B-3g Standardizing a Random Variable696

B-3h Skewness and Kurtosis697

B-4 Features of Joint and Conditional Distributions697

B-4a Measures of Association：Covariance and Correlation697

B-4b Covariance697

B-4c Correlation Coefficient698

B-4d Variance of Sums of Random Variables699

B-4e Conditional Expectation700

B-4f Properties of Conditional Expectation702

B-4g Conditional Variance704

B-5 The Normal and Related Distributions704

B-5a The Normal Distribution704

B-5b The Standard Normal Distribution705

B-5c Additional Properties of the Normal Distribution707

B-5d The Chi-Square Distribution708

B-5e The t Distribution708

B-5f The F Distribution709

Summary711

Key Terms711

Problems711

MATH REFRESHER C Fundamentals of Mathematical Statistics714

C-1 Populations，Parameters，and Random Sampling714

C-1 a Sampling714

C-2 Finite Sample Properties of Estimators715

C-2a Estimators and Estimates715

C-2b Unbiasedness716

C-2C The Sampling Variance of Estimators718

C-2d Efficiency719

C-3 Asymptotic or Large Sample Properties of Estimators721

C-3a Consistency721

C-3b Asymptotic Normality723

C-4 General Approaches to Parameter Estimation724

C-4a Method of Moments725

C-4b Maximum Likelihood725

C-4c Least Squares726

C-5 Interval Estimation and Confidence Intervals727

C-5a The Nature of Interval Estimation727

C-5b Confidence Intervals for the Mean from a Normally Distributed Population729

C-5c A Simple Rule of Thumbfor a 95onfidence Interval731

C-5d Asymptotic Confidence Intervals for Nonnormal Populations732

C-6 Hypothesis Testing733

C-6a Fundamentals of Hypothesis Testing733

C-6b Testing Hypotheses about the Mean in a Normal Population735

C-6c Asymptotic Tests for Nonnormal Populations738

C-6d Computing and Using p- Values738

C-6e The Relationship between Confidence Intervals and Hypothesis Testing741

C-6f Practical versus Statistical Significance742

C-7 Remarks on Notation743

Summary743

Key Terms744

Problems744

ADVANCED TREATMENT D Summary of Matrix Algebra749

D-1 Basic Definitions749

D-2 Matrix Operations750

D-2a Matrix Addition750

D-2b Scalar Multiplication750

D-2c Matrix Multiplication751

D-2d Transpose752

D-2e Partitioned Matrix Multiplication752

D-2f Trace753

D-2g Inverse753

D-3 Linear Independence and Rank of a Matrix754

D-4 Quadratic Forms and Positive Definite Matrices754

D-5 Idempotent Matrices755

D-6 Differentiation of Linear and Quadratic Forms755

D-7 Moments and Distributions of Random Vectors756

D-7a Expected Value756

D-7b Variance-Covariance Matrix756

D-7c Multivariate Normal Distribution756

D-7d Chi-Square Distribution757

D-7e t Distribution757

D-7f F Distribution757

Summary757

Key Terms757

Problems758

ADVANCED TREATMENT E The Linear Regression Model in Matrix Form760

E-1 The Model and Ordinary Least Squares Estimation760

E-1 a The Frisch-Waugh Theorem762

E-2 Finite Sample Properties of OLS763

E-3 Statistical Inference767

E-4 Some Asymptotic Analysis769

E-4a Wald Statistics for Testing Multiple Hypotheses771

Summary771

Key Terms771

Problems772

Answers to Going Further Questions775

Statistical Tables784

References791

Glossary797

Index812