Introductory Econometrics: A Modern Approach

Cover......Page 1
Half Title......Page 2
Title......Page 4
Statement......Page 5
Copyright......Page 6
Brief Contents......Page 7
Contents......Page 8
Preface......Page 17
About the Author......Page 27
1.1 What Is Econometrics?......Page 29
1.2 Steps in Empirical Economic Analysis......Page 30
1.3 The Structure of Economic Data......Page 33
1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis......Page 40
Summary......Page 44
Computer Exercises......Page 45
Introduction......Page 49
2.1 Definition of the Simple Regression Model......Page 50
2.2 Deriving the Ordinary Least Squares Estimates......Page 55
2.3 Properties of OLS on Any Sample of Data......Page 63
2.4 Units of Measurement and Functional Form......Page 67
2.5 Expected Values and Variances of the OLS Estimators......Page 73
2.6 Regression through the Origin and Regression on a Constant......Page 85
Summary......Page 86
Key Terms......Page 87
Problems......Page 88
Computer Exercises......Page 91
Introduction......Page 96
3.1 Motivation for Multiple Regression......Page 97
3.2 Mechanics and Interpretation of Ordinary Least Squares......Page 100
3.3 The Expected Value of the OLS Estimators......Page 111
3.4 The Variance of the OLS Estimators......Page 121
3.5 Efficiency of OLS: The Gauss-Markov Theorem......Page 129
3.6 Some Comments on the Language of Multiple Regression Analysis......Page 131
Summary......Page 132
Key Terms......Page 133
Problems......Page 134
Computer Exercises......Page 138
4.1 Sampling Distributions of the OLS Estimators......Page 146
4.2 Testing Hypotheses about a Single Population Parameter: The t Test......Page 149
4.3 Confidence Intervals......Page 166
4.4 Testing Hypotheses about a Single Linear Combination of the Parameters......Page 168
4.5 Testing Multiple Linear Restrictions: The F Test......Page 171
4.6 Reporting Regression Results......Page 182
Summary......Page 185
Problems......Page 187
Computer Exercises......Page 192
Introduction......Page 196
5.1 Consistency......Page 197
5.2 Asymptotic Normality and Large Sample Inference......Page 201
5.3 Asymptotic Efficiency of OLS......Page 209
Summary......Page 210
Computer Exercises......Page 211
6.1 Effects of Data Scaling on OLS Statistics......Page 214
6.2 More on Functional Form......Page 219
6.3 More on Goodness-of-Fit and Selection of Regressors......Page 228
6.4 Prediction and Residual Analysis......Page 235
Summary......Page 244
Key Terms......Page 245
Problems......Page 246
Computer Exercises......Page 248
7.1 Describing Qualitative Information......Page 255
7.2 A Single Dummy Independent Variable......Page 256
7.3 Using Dummy Variables for Multiple Categories......Page 263
7.4 Interactions Involving Dummy Variables......Page 268
7.5 A Binary Dependent Variable: The Linear Probability Model......Page 276
7.6 More on Policy Analysis and Program Evaluation......Page 281
7.7 Interpreting Regression Results with Discrete Dependent Variables......Page 284
Summary......Page 285
Problems......Page 286
Computer Exercises......Page 290
8.1 Consequences of Heteroskedasticity for OLS......Page 296
8.2 Heteroskedasticity-Robust Inference after OLS Estimation......Page 297
8.3 Testing for Heteroskedasticity......Page 303
8.4 Weighted Least Squares Estimation......Page 308
8.5 The Linear Probability Model Revisited......Page 322
Summary......Page 324
Problems......Page 325
Computer Exercises......Page 327
Introduction......Page 331
9.1 Functional Form Misspecification......Page 332
9.2 Using Proxy Variables for Unobserved Explanatory Variables......Page 336
9.3 Models with Random Slopes......Page 343
9.4 Properties of OLS under Measurement Error......Page 345
9.5 Missing Data, Nonrandom Samples, and Outlying Observations......Page 352
9.6 Least Absolute Deviations Estimation......Page 359
Summary......Page 362
Problems......Page 363
Computer Exercises......Page 366
Introduction......Page 371
10.1 The Nature of Time Series Data......Page 372
10.2 Examples of Time Series Regression Models......Page 373
10.3 Finite Sample Properties of OLS under Classical Assumptions......Page 377
10.4 Functional Form, Dummy Variables, and Index Numbers......Page 384
10.5 Trends and Seasonality......Page 391
Summary......Page 401
Key Terms......Page 402
Problems......Page 403
Computer Exercises......Page 405
Introduction......Page 408
11.1 Stationary and Weakly Dependent Time Series......Page 409
11.2 Asymptotic Properties of OLS......Page 412
11.3 Using Highly Persistent Time Series in Regression Analysis......Page 419
11.4 Dynamically Complete Models and the Absence of Serial Correlation......Page 427
Summary......Page 430
Problems......Page 432
Computer Exercises......Page 435
12.1 Properties of OLS with Serially Correlated Errors......Page 440
12.2 Testing for Serial Correlation......Page 444
12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors......Page 451
12.4 Differencing and Serial Correlation......Page 457
12.5 Serial Correlation-Robust Inference after OLS......Page 459
12.6 Heteroskedasticity in Time Series Regressions......Page 462
Summary......Page 467
Problems......Page 468
Computer Exercises......Page 469
Introduction......Page 475
Introduction......Page 476
13.1 Pooling Independent Cross Sections across Time......Page 477
13.2 Policy Analysis with Pooled Cross Sections......Page 482
13.3 Two-Period Panel Data Analysis......Page 487
13.4 Policy Analysis with Two-Period Panel Data......Page 493
13.5 Differencing with More Than Two Time Periods......Page 496
Problems......Page 502
Computer Exercises......Page 504
14.1 Fixed Effects Estimation......Page 512
14.2 Random Effects Models......Page 520
14.3 The Correlated Random Effects Approach......Page 525
14.4 Applying Panel Data Methods to Other Data Structures......Page 527
Summary......Page 529
Problems......Page 530
Computer Exercises......Page 531
Introduction......Page 540
15.1 Motivation: Omitted Variables in a Simple Regression Model......Page 541
15.2 IV Estimation of the Multiple Regression Model......Page 552
15.3 Two Stage Least Squares......Page 556
15.4 IV Solutions to Errors-in-Variables Problems......Page 560
15.5 Testing for Endogeneity and Testing Overidentifying Restrictions......Page 562
15.7 Applying 2SLS to Time Series Equations......Page 566
15.8 Applying 2SLS to Pooled Cross Sections and Panel Data......Page 568
Summary......Page 570
Problems......Page 571
Computer Exercises......Page 574
Introduction......Page 582
16.1 The Nature of Simultaneous Equations Models......Page 583
16.2 Simultaneity Bias in OLS......Page 586
16.3 Identifying and Estimating a Structural Equation......Page 588
16.4 Systems with More Than Two Equations......Page 595
16.5 Simultaneous Equations Models with Time Series......Page 596
16.6 Simultaneous Equations Models with Panel Data......Page 600
Summary......Page 602
Problems......Page 603
Computer Exercises......Page 606
Introduction......Page 611
17.1 Logit and Probit Models for Binary Response......Page 612
17.2 The Tobit Model for Corner Solution Responses......Page 624
17.3 The Poisson Regression Model......Page 632
17.4 Censored and Truncated Regression Models......Page 637
17.5 Sample Selection Corrections......Page 643
Summary......Page 649
Problems......Page 650
Computer Exercises......Page 652
Introduction......Page 660
18.1 Infinite Distributed Lag Models......Page 661
18.2 Testing for Unit Roots......Page 667
18.3 Spurious Regression......Page 672
18.4 Cointegration and Error Correction Models......Page 674
18.5 Forecasting......Page 680
Summary......Page 695
Problems......Page 697
Computer Exercises......Page 699
19.1 Posing a Question......Page 704
19.2 Literature Review......Page 706
19.3 Data Collection......Page 707
19.4 Econometric Analysis......Page 711
19.5 Writing an Empirical Paper......Page 714
Sample Empirical Projects......Page 722
List of Journals......Page 728
Data Sources......Page 729
Appendix A: Basic Mathematical Tools......Page 731
Appendix B: Fundamentals of Probability......Page 750
Appendix C: Fundamentals of Mathematical Statistics......Page 783
Appendix D: Summary of Matrix Algebra......Page 824
Appendix E: The Linear Regression Model in Matrix Form......Page 835
Appendix F: Answers to Chapter Questions......Page 849
Appendix G: Statistical Tables......Page 859
References......Page 866
Glossary......Page 872
Index......Page 890