Econometric Analysis

Cover......Page 1
Title Page......Page 3
Copyright Page......Page 4
Contents......Page 8
Examples and Applications......Page 27
Preface......Page 35
ACKNOWLEDGMENTS......Page 40
1.2 The Paradigm of Econometrics......Page 43
1.3 The Practice of Econometrics......Page 45
1.4 Econometric Modeling......Page 46
1.5 Plan of the Book......Page 49
1.6 Preliminaries......Page 51
2.1 Introduction......Page 53
2.2 The Linear Regression Model......Page 54
2.3 Assumptions of the Linear Regression Model......Page 57
2.4 Summary and Conclusions......Page 67
3.2 Least Squares Regression......Page 68
3.3 Partitioned Regression and Partial Regression......Page 74
3.4 Partial Regression and Partial Correlation Coefficients......Page 78
3.5 Goodness of Fit and the Analysis of Variance......Page 81
3.6 Linearly Transformed Regression......Page 88
3.7 Summary and Conclusions......Page 89
4.1 Introduction......Page 93
4.2 Motivating Least Squares......Page 94
4.3 Finite Sample Properties of Least Squares......Page 96
4.4 Large Sample Properties of the Least Squares Estimator......Page 105
4.5 Interval Estimation......Page 117
4.6 Prediction and Forecasting......Page 122
4.7 Data Problems......Page 130
4.8 Summary and Conclusions......Page 144
5.2 Hypothesis Testing Methodology......Page 150
5.3 Two Approaches to Testing Hypotheses......Page 154
5.4 Wald Tests Based on the Distance Measure......Page 157
5.5 Testing Restrictions Using the Fit of the Regression......Page 163
5.6 Nonnormal Disturbances and Large-Sample Tests......Page 169
5.7 Testing Nonlinear Restrictions......Page 173
5.8 Choosing between Nonnested Models......Page 176
5.9 A Specification Test......Page 179
5.10 Model Building—A General to Simple Strategy......Page 180
5.11 Summary and Conclusions......Page 185
6.2 Using Binary Variables......Page 191
6.3 Nonlinearity in the Variables......Page 200
6.4 Modeling and Testing for a Structural Break......Page 210
6.5 Summary and Conclusions......Page 217
7.1 Introduction......Page 223
7.2 Nonlinear Regression Models......Page 224
7.3 Median and Quantile Regression......Page 244
7.4 Partially Linear Regression......Page 252
7.5 Nonparametric Regression......Page 254
7.6 Summary and Conclusions......Page 257
8.1 Introduction......Page 261
8.2 Assumptions of the Extended Model......Page 265
8.3 Estimation......Page 266
8.4 Two Specification Tests......Page 275
8.5 Measurement Error......Page 281
8.6 Nonlinear Instrumental Variables Estimation......Page 288
8.7 Weak Instruments......Page 291
8.8 Natural Experiments and the Search for Causal Effects......Page 293
8.9 Summary and Conclusions......Page 296
9.1 Introduction......Page 299
9.2 Inefficient Estimation by Least Squares and Instrumental Variables......Page 300
9.3 Efficient Estimation by Generalized Least Squares......Page 306
9.4 Heteroscedasticity and Weighted Least Squares......Page 310
9.5 Testing for Heteroscedasticity......Page 317
9.6 Weighted Least Squares......Page 319
9.7 Applications......Page 322
9.8 Summary and Conclusions......Page 327
10.1 Introduction......Page 332
10.2 The Seemingly Unrelated Regressions Model......Page 334
10.3 Seemingly Unrelated Generalized Regression Models......Page 346
10.4 Nonlinear Systems of Equations......Page 347
10.5 Systems of Demand Equations: Singular Systems......Page 349
10.6 Simultaneous Equations Models......Page 356
10.7 Summary and Conclusions......Page 378
11.1 Introduction......Page 385
11.2 Panel Data Models......Page 386
11.3 The Pooled Regression Model......Page 391
11.4 The Fixed Effects Model......Page 401
11.5 Random Effects......Page 412
11.6 Nonspherical Disturbances and Robust Covariance Estimation......Page 427
11.7 Spatial Autocorrelation......Page 431
11.8 Endogeneity......Page 436
11.9 Nonlinear Regression with Panel Data......Page 453
11.10 Systems of Equations......Page 457
11.11 Parameter Heterogeneity......Page 458
11.12 Summary and Conclusions......Page 468
12.1 Introduction......Page 474
12.2 Parametric Estimation and Inference......Page 476
12.3 Semiparametric Estimation......Page 481
12.4 Nonparametric Estimation......Page 486
12.5 Properties of Estimators......Page 489
12.6 Summary and Conclusions......Page 496
13.1 Introduction......Page 497
13.2 Consistent Estimation: The Method of Moments......Page 498
13.3 Minimum Distance Estimation......Page 505
13.4 The Generalized Method of Moments (GMM) Estimator......Page 510
13.5 Testing Hypotheses in the GMM Framework......Page 521
13.6 GMM Estimation of Econometric Models......Page 524
13.7 Summary and Conclusions......Page 549
14.2 The Likelihood Function and Identification of the Parameters......Page 551
14.3 Efficient Estimation: The Principle of Maximum Likelihood......Page 553
14.4 Properties of Maximum Likelihood Estimators......Page 555
14.5 Conditional Likelihoods, Econometric Models, and the GMM Estimator......Page 565
14.6 Hypothesis and Specification Tests and Fit Measures......Page 566
14.7 Two-Step Maximum Likelihood Estimation......Page 578
14.8 Pseudo-Maximum Likelihood Estimation and Robust Asymptotic Covariance Matrices......Page 584
14.9 Applications of Maximum Likelihood Estimation......Page 590
14.10 Latent Class and Finite Mixture Models......Page 630
14.11 Summary and Conclusions......Page 640
15.1 Introduction......Page 645
15.2 Random Number Generation......Page 647
15.3 Simulation-Based Statistical Inference: The Method of Krinsky and Robb......Page 651
15.4 Bootstrapping Standard Errors and Confidence Intervals......Page 653
15.5 Monte Carlo Studies......Page 657
15.6 Simulation-Based Estimation......Page 663
15.7 A Random Parameters Linear Regression Model......Page 676
15.8 Hierarchical Linear Models......Page 681
15.9 Nonlinear Random Parameter Models......Page 683
15.10 Individual Parameter Estimates......Page 684
15.11 Mixed Models and Latent Class Models......Page 692
15.12 Summary and Conclusions......Page 695
16.1 Introduction......Page 697
16.2 Bayes Theorem and the Posterior Density......Page 698
16.3 Bayesian Analysis of the Classical Regression Model......Page 700
16.4 Bayesian Inference......Page 706
16.5 Posterior Distributions and the Gibbs Sampler......Page 710
16.6 Application: Binomial Probit Model......Page 713
16.7 Panel Data Application: Individual Effects Models......Page 716
16.8 Hierarchical Bayes Estimation of a Random Parameters Model......Page 718
16.9 Summary and Conclusions......Page 720
17.1 Introduction......Page 723
17.2 Models for Binary Outcomes......Page 725
17.3 Estimation and Inference in Binary Choice Models......Page 732
17.4 Binary Choice Models for Panel Data......Page 758
17.5 Bivariate and Multivariate Probit Models......Page 780
17.6 Summary and Conclusions......Page 797
18.1 Introduction......Page 802
18.2 Models for Unordered Multiple Choices......Page 803
18.3 Random Utility Models for Ordered Choices......Page 826
18.4 Models for Counts of Events......Page 844
18.5 Summary and Conclusions......Page 871
19.2 Truncation......Page 875
19.3 Censored Data......Page 887
19.4 Models for Duration......Page 903
19.5 Incidental Truncation and Sample Selection......Page 914
19.6 Evaluating Treatment Effects......Page 930
19.7 Summary and Conclusions......Page 940
20.1 Introduction......Page 945
20.2 The Analysis of Time-Series Data......Page 948
20.3 Disturbance Processes......Page 951
20.4 Some Asymptotic Results for Analyzing Time-Series Data......Page 954
20.5 Least Squares Estimation......Page 960
20.6 GMM Estimation......Page 963
20.7 Testing for Autocorrelation......Page 964
20.8 Efficient Estimation When Ω Is Known\n......Page 966
20.9 Estimation When Ω Is Unknown\n......Page 968
20.10 Autoregressive Conditional Heteroscedasticity......Page 972
20.11 Summary and Conclusions......Page 981
21.2 Nonstationary Processes and Unit Roots......Page 984
21.3 Cointegration......Page 1001
21.4 Nonstationary Panel Data......Page 1012
21.5 Summary and Conclusions......Page 1013
A.2 Algebraic Manipulation of Matrices......Page 1015
A.3 Geometry of Matrices......Page 1021
A.4 Solution of a System of Linear Equations......Page 1031
A.5 Partitioned Matrices......Page 1034
A.6 Characteristic Roots and Vectors......Page 1037
A.7 Quadratic Forms and Definite Matrices......Page 1046
A.8 Calculus and Matrix Algebra......Page 1049
B.2 Random Variables......Page 1057
B.3 Expectations of a Random Variable......Page 1059
B.4 Some Specific Probability Distributions......Page 1061
B.5 The Distribution of a Function of a Random Variable......Page 1068
B.6 Representations of a Probability Distribution......Page 1070
B.7 Joint Distributions......Page 1072
B.8 Conditioning in a Bivariate Distribution......Page 1076
B.9 The Bivariate Normal Distribution......Page 1079
B.10 Multivariate Distributions......Page 1080
B.11 The Multivariate Normal Distribution......Page 1083
C.1 Introduction......Page 1089
C.3 Descriptive Statistics......Page 1090
C.4 Statistics as Estimators—Sampling Distributions......Page 1093
C.5 Point Estimation of Parameters......Page 1097
C.6 Interval Estimation......Page 1102
C.7 Hypothesis Testing......Page 1104
D.1 Introduction......Page 1108
D.2 Large-Sample Distribution Theory......Page 1109
D.3 Asymptotic Distributions......Page 1126
D.4 Sequences and the Order of a Sequence......Page 1130
E.1 Introduction......Page 1131
E.2 Computation in Econometrics......Page 1132
E.3 Optimization......Page 1135
E.4 Examples......Page 1148
Appendix F: Data Sets Used in Applications......Page 1151
References......Page 1157
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