Econometric analysis:
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston ; Munich [u.a.]
Pearson
2012
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| Ausgabe: | 7. ed., internat. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 1155 - 1210. - Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | 1238 S. graph. Darst. |
| ISBN: | 9780273753568 0273753568 9780131395381 |
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| 264 | 1 | |a Boston ; Munich [u.a.] |b Pearson |c 2012 | |
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| 650 | 7 | |a Econometrie |2 gtt | |
| 650 | 4 | |a Économétrie | |
| 650 | 4 | |a Econometrics | |
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Datensatz im Suchindex
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| DE-BY-FWS_katkey | 434161 |
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| _version_ | 1824553869713080320 |
| adam_text | Titel: Econometric analysis
Autor: Greene, William H.
Jahr: 2012
BRIEF CONTENTS
Examples and Applications
Preface 33
25
Part I The Linear Regression Model
Chapter I Econometrics 41
Chapter 2 The Linear Regression Model 51
Chapter 3 Least Squares 66
Chapter 4 The Least Squares Estimator 91
Chapter 5 Hypothesis Tests and Model Selection 148
Chapter 6 Functional Form and Structural Change 189
Chapter 7 Nonlinear, Semiparametric, and Nonparametric
Regression Models 221
Chapter 8 Endogeneity and Instrumental Variable Estimation
259
Part II Generalized Regression Model and Equation Systems
Chapter 9 The Generalized Regression Model and Heteroscedasticity
Chapter 10 Systems of Equations 330
Chapter 11 Models for Panel Data 383
Part III Estimation Methodology
Chapter 12 Estimation Frameworks in Econometrics 472
Chapter 13 Minimum Distance Estimation and the Generalized
Method of Moments 495
Chapter 14 Maximum Likelihood Estimation 549
Chapter 15 Simulation-Based Estimation and Inference and Random
Parameter Models 643
Chapter 16 Bayesian Estimation and Inference 695
Part IV Cross Sections, Panel Data, and Microeconometrics
Chapter 17 Discrete Choice 721
Chapter 18 Discrete Choices and Event Counts 800
Chapter 19 Limited Dependent Variables—Truncation, Censoring,
and Sample Selection 873
297
Brief Contents 5
Part V lime Series and Macroeconometrics
Chapter 20 Serial Correlation 943
Chapter 21 Nonstationary Data 982
Part VI Appendices
Appendix A Matrix Algebra 1013
Appendix B Probability and Distribution Theory 1055
Appendix C Estimation and Inference 1087
Appendix D Large-Sample Distribution Theory 1106
Appendix E Computation and Optimization 1129
Appendix F Data Sets Used in Applications 1149
References 1155
Combined Author and Subject Index 1201
CONTENTS
Examples and Applications 25
Preface 33
PART I The Linear Regression Model
CHAPTER 1 Econometrics 41
1.1 Introduction 41
1.2 The Paradigm of Econometrics 41
1.3 The Practice of Econometrics 43
1.4 Econometric Modeling 44
1.5 Plan of the Book 47
1.6 Preliminaries 49
1.6.1 Numerical Examples 49
1.6.2 Software and Replication 49
1.6.3 Notational Conventions 49
CHAPTER 2 The Linear Regression Model 51
2.1 Introduction 51
2.2 The Linear Regression Model 52
2.3 Assumptions of the Linear Regression Model 55
2.3.1 Linearity of the Regression Model 55
2.3.2 Full Rank 59
2.3.3 Regression 60
2.3.4 Spherical Disturbances 61
2.3.5 Data Generating Process for the Regressors 63
2.3.6 Normality 63
2.3.7 Independence 64
2.4 Summary and Conclusions 65
CHAPTER 3 Least Squares 66
3.1 Introduction 66
3.2 Least Squares Regression 66
3.2.1 The Least Squares Coefficient Vector 67
Contents 7
3.2.2 Application: An Investment Equation 68
3.2.3 Algebraic Aspects of the Least Squares Solution 70
3.2.4 Projection 71
3.3 Partitioned Regression and Partial Regression 72
3.4 Partial Regression and Partial Correlation Coefficients 76
3.5 Goodness of Fit and the Analysis of Variance 79
5.5.7 The Adjusted R-Squared and a Measure of Fit 82
3.5.2 R-Squared and the Constant Term in the Model 84
3.5.3 Comparing Models 85
3.6 Linearly Transformed Regression 86
3.7 Summary and Conclusions 87
CHAPTER 4 The Least Squares Estimator 91
4.1 Introduction 91
4.2 Motivating Least Squares 92
4.2.1 The Population Orthogonality Conditions 92
4.2.2 Minimum Mean Squared Error Predictor 93
4.2.3 Minimum Variance Linear Unbiased Estimation 94
4.3 Finite Sample Properties of Least Squares 94
4.3.1 Unbiased Estimation 95
4.3.2 Bias Caused by Omission of Relevant Variables 96
4.3.3 Inclusion of Irrelevant Variables 98
4.3.4 The Variance of the Least Squares Estimator 98
4.3.5 The Gauss-Markov Theorem 100
4.3.6 The Implications of Stochastic Regressors 100
4.3.7 Estimating the Variance of the Least Squares Estimator 101
4.3.8 The Normality Assumption 103
A A Large Sample Properties of the Least Squares Estimator 103
4.4.1 Consistency of the Least Squares Estimator of ß 103
4.4.2 Asymptotic Normality of the Least Squares Estimator 105
4.4.3 Consistency ofs2 and the Estimator ofAsy. Var[b] 107
4.4.4 Asymptotic Distribution of a Function ofb: The Delta
Method 108
4.4.5 Asymptotic Efficiency 109
4.4.6 Maximum Likelihood Estimation 113
4.5 Interval Estimation 115
4.5.1 Forming a Confidence Interval for a Coefficient 116
4.5.2 Confidence Intervals Based on Large Samples 118
4.5.3 Confidence Interval for a Linear Combination of Coefficients:
The Oaxaca Decomposition 119
4.6 Prediction and Forecasting 120
4.6.1 Prediction Intervals 121
4.6.2 Predicting y When the Regression Model Describes Log y 121
8 Contents
4.6.3 Prediction Interval for y When the Regression Model Describes
Logy 123
4.6.4 Forecasting 127
4.7 Data Problems 128
4.7.1 Multicollinearity 129
4.7.2 Pretest Estimation 131
4.7.3 Principal Components 132
4.7.4 Missing Values and Data Imputation 134
4.7.5 Measurement Error 137
4.7.6 Outliers and Influential Observations 139
4.8 Summary and Conclusions 142
CHAPTER 5 Hypothesis Tests and Model Selection 148
5.1 Introduction 148
5.2 Hypothesis Testing Methodology 148
5.2.1 Restrictions and Hypotheses 149
5.2.2 Nested Models 150
5.2.3 Testing Procedures—Ney man-Pearson Methodology 151
5.2.4 Size, Power, and Consistency of a Test 151
5.2.5 A Methodological Dilemma: Bayesian versus Classical Testing
152
5.3 Two Approaches to Testing Hypotheses 152
5.4 Wald Tests Based on the Distance Measure 155
5.4.1 Testing a Hypothesis about a Coefficient 155
5.4.2 The F Statistic and the Least Squares Discrepancy 157
5.5 Testing Restrictions Using the Fit of the Regression 161
5.5.7 The Restricted Least Squares Estimator 161
5.5.2 The Loss of Fit from Restricted Least Squares 162
5.5.3 Testing the Significance of the Regression 166
5.5.4 Solving Out the Restrictions and a Caution about
Using R2 166
5.6 Nonnormal Disturbances and Large-Sample Tests 167
5.7 Testing Nonlinear Restrictions 171
5.8 Choosing between Nonnested Models 174
5.8.1 Testing Nonnested Hypotheses 174
5.8.2 An Encompassing Model 175
5.8.3 Comprehensive Approach—TheJ Test 176
5.9 A Specification Test 177
5.10 Model Building—A General to Simple Strategy 178
5.10.1 Model Selection Criteria 179
5.10.2 Model Selection 180
5.10.3 Classical Model Selection 180
5.10.4 Bayesian Model Averaging 181
5.11 Summary and Conclusions 183
Contents 9
CHAPTER 6 Functional Form and Structural Change 189
6.1 Introduction 189
6.2 Using Binary Variables 189
6.2.7 Binary Variables in Regression 189
6.2.2 Several Categories 192
6.2.3 Several Groupings 192
6.2.4 Threshold Effects and Categorical Variables 194
6.2.5 Treatment Effects and Differences in Differences
Regression 195
6.3 Nonlinearity in the Variables 198
6.3.1 Piecewise Linear Regression 198
6.3.2 Functional Forms 200
6.3.3 Interaction Effects 201
6.3.4 Identifying Nonlinearity 202
6.3.5 Intrinsically Linear Models 205
6.4 Modeling and Testing for a Structural Break 208
6.4.1 Different Parameter Vectors 208
6.4.2 Insufficient Observations 209
6.4.3 Change in a Subset of Coefficients 210
6.4.4 Tests of Structural Break with Unequal Variances 211
6.4.5 Predictive Test of Model Stability 214
6.5 Summary and Conclusions 215
CHAPTER 7 Nonlinear, Semiparametric, and Nonparametric
Regression Models 221
7.1 Introduction 221
7.2 Nonlinear Regression Models 222
7.2.1 Assumptions of the Nonlinear Regression Model 222
7.2.2 The Nonlinear Least Squares Estimator 224
7.2.3 Large Sample Properties of the Nonlinear Least Squares
Estimator 226
7.2.4 Hypothesis Testing and Parametric Restrictions 229
7.2.5 Applications 231
7.2.6 Computing the Nonlinear Least Squares Estimator 240
7.3 Median and Quantile Regression 242
7.3.1 Least Absolute Deviations Estimation 243
7.3.2 Quantile Regression Models 247
7.4 Partially Linear Regression 250
7.5 Nonparametric Regression 252
7.6 Summary and Conclusions 255
CHAPTER 8 Endogeneity and Instrumental Variable Estimation 259
8.1 Introduction 259
8.2 Assumptions of the Extended Model 263
IO Contents
8.3 Estimation 264
8.3.1 Least Squares 265
8.3.2 The Instrumental Variables Estimator 265
8.3.3 Motivating the Instrumental Variables Estimator 267
8.3.4 Two-Stage Least Squares 270
8.4 Two Specification Tests 273
8.4.1 The Hausman and Wu Specification Tests 274
8.4.2 A Test for Overidentification 278
8.5 Measurement Error 279
8.5.1 Least Squares Attenuation 280
8.5.2 Instrumental Variables Estimation 282
8.5.3 Proxy Variables 282
8.6 Nonlinear Instrumental Variables Estimation 286
8.7 Weak Instruments 289
8.8 Natural Experiments and the Search for Causal Effects 291
8.9 Summary and Conclusions 294
PART II Generalized Regression Model and Equation Systems
CHAPTER 9 The Generalized Regression Model and Heteroscedastkity 297
9.1 Introduction 297
9.2 Inefficient Estimation by Least Squares and Instrumental
Variables 298
9.2.1 Finite-Sample Properties of Ordinary Least Squares 299
9.2.2 Asymptotic Properties of Ordinary Least Squares 299
9.2.3 Robust Estimation of Asymptotic Covariance Matrices 301
9.2.4 Instrumental Variable Estimation 302
9.3 Efficient Estimation by Generalized Least Squares 304
9.3.1 Generalized Least Squares (GLS) 304
9.3.2 Feasible Generalized Least Squares (FGLS) 306
9.4 Heteroscedasticity and Weighted Least Squares 308
9.4.1 Ordinary Least Squares Estimation 309
9.4.2 Inefficiency of Ordinary Least Squares 310
9.4.3 The Estimated Covariance Matrix ofb 310
9.4.4 Estimating the Appropriate Covariance Matrix for Ordinary
Least Squares 312
9.5 Testing for Heteroscedasticity 315
9.5.1 White s General Test 315
9.5.2 The Breusch-Pagan/Godfrey LM Test 316
9.6 Weighted Least Squares 317
9.6.7 Weighted Least Squares with Known fl 318
9.6.2 Estimation When ñ Contains Unknown Parameters 319
Contents 11
9.7 Applications 320
9.7.7 Multiplicative Heteroscedasticity 320
9.7.2 Groupwise Heteroscedasticity 322
9.8 Summary and Conclusions 325
CHAPTER 10 Systems of Equations 330
10.1 Introduction 330
10.2 The Seemingly Unrelated Regressions Model 332
10.2.1 Generalized Least Squares 333
10.2.2 Seemingly Unrelated Regressions with Identical Regressors 335
10.2.3 Feasible Generalized Least Squares 336
10.2.4 Testing Hypotheses 336
10.2.5 A Specification Test for the SUR Model 337
10.2.6 The Pooled Model 339
10.3 Seemingly Unrelated Generalized Regression Models 344
10.4 Nonlinear Systems of Equations 345
10.5 Systems of Demand Equations: Singular Systems 347
10.5.1 Cobb-Douglas Cost Function 347
10.5.2 Flexible Functional Forms: The Translog Cost Function 350
10.6 Simultaneous Equations Models 354
10.6.1 Systems of Equations 355
10.6.2 A General Notation for Linear Simultaneous Equations
Models 358
10.6.3 The Problem of Identification 361
10.6.4 Single Equation Estimation and Inference 366
10.6.5 System Methods of Estimation 369
10.6.6 Testing in the Presence of Weak Instruments 374
10.7 Summary and Conclusions 376
CHAPTER 11 Models for Panel Data 383
11.1 Introduction 383
11.2 Panel Data Models 384
11.2.1 General Modeling Framework for Analyzing Panel Data 385
11.2.2 Model Structures 386
11.2.3 Extensions 387
11.2.4 Balanced and Unbalanced Panels 388
11.2.5 Weil-Behaved Panel Data 388
11.3 The Pooled Regression Model 389
11.3.1 Least Squares Estimation of the Pooled Model 389
11.3.2 Robust Covariance Matrix Estimation 390
11.3.3 Clustering and Stratification 392
11.3.4 Robust Estimation Using Group Means 394
12 Contents
11.3.5 Estimation with First Differences 395
11.3.6 The Within- and Between-Groups Estimators 397
11.4 The Fixed Effects Model 399
11.4.1 Least Squares Estimation 400
11.4.2 Small T Asymptotics 402
11.4.3 Testing the Significance of the Group Effects 403
11.4.4 Fixed Time and Group Effects 403
11.4.5 Time-Invariant Variables and Fixed Effects Vector
Decomposition 404
11.5 Random Effects 410
11.5.1 Least Squares Estimation 412
11.5.2 Generalized Least Squares 413
11.5.3 Feasible Generalized Least Squares When X Is Unknown 414
11.5.4 Testing for Random Effects 416
11.5.5 Hausman s Specification Test for the Random Effects
Model 419
11.5.6 Extending the Unobserved Effects Model: Mundlak s
Approach 420
11.5.7 Extending the Random and Fixed Effects Models:
Chamberlain s Approach 421
11.6 Nonspherical Disturbances and Robust Covariance Estimation 425
11.6.1 Robust Estimation of the Fixed Effects Model 425
11.6.2 Heteroscedasticity in the Random Effects Model 427
11.6.3 Autocorrelation in Panel Data Models 428
11.6.4 Cluster (and Panel) Robust Covariance Matrices for Fixed and
Random Effects Estimators 428
11.7 Spatial Autocorrelation 429
11.8 Endogeneity 434
11.8.1 Hausman and Taylor s Instrumental Variables Estimator 434
11.8.2 Consistent Estimation of Dynamic Panel Data Models:
Anderson and Hsiao s IV Estimator 438
11.8.3 Efficient Estimation of Dynamic Panel Data Models— The
Arellano/Bond Estimators 440
11.8.4 Nonstationary Data and Panel Data Models 450
11.9 Nonlinear Regression with Panel Data 451
11.9.1 A Robust Covariance Matrix for Nonlinear Least Squares 451
11.9.2 Fixed Effects 452
11.9.3 Random Effects 454
11.10 Systems of Equations 455
11.11 Parameter Heterogeneity 456
11.11.1 The Random Coefficients Model 457
11.11.2 A Hierarchical Linear Model 460
11.11.3 Parameter Heterogeneity and Dynamic Panel Data
Models 461
11.12 Summary and Conclusions 466
Contents 13
PART III Estimation Methodology
CHAPTER 12 Estimation Frameworks in Econometrics 472
12.1 Introduction 472
12.2 Parametric Estimation and Inference 474
12.2.1 Classical Likelihood-Based Estimation 474
12.2.2 Modeling Joint Distributions with Copula Functions 476
12.3 Semiparametric Estimation 479
12.3.1 GMM Estimation in Econometrics 479
12.3.2 Maximum Empirical Likelihood Estimation 480
12.3.3 Least Absolute Deviations Estimation and Quantile
Regression 481
12.3.4 Kernel Density Methods 482
12.3.5 Comparing Parametric and Semiparametric Analyses 483
12.4 Nonparametric Estimation 484
12.4.1 Kernel Density Estimation 485
12.5 Properties of Estimators 487
12.5.1 Statistical Properties of Estimators 488
12.5.2 Extremum Estimators 489
12.5.3 Assumptions for Asymptotic Properties of Extremum
Estimators 489
12.5.4 Asymptotic Properties of Estimators 492
12.5.5 Testing Hypotheses 493
12.6 Summary and Conclusions 494
CHAPTER 13 Minimum Distance Estimation and the Generalized
Method of Moments 495
13.1 Introduction 495
13.2 Consistent Estimation: The Method of Moments 496
13.2.1 Random Sampling and Estimating the Parameters of
Distributions 497
13.2.2 Asymptotic Properties of the Method of Moments
Estimator 501
13.2.3 Summary—The Method of Moments 503
13.3 Minimum Distance Estimation 503
13.4 The Generalized Method of Moments (GMM) Estimator 508
13.4.1 Estimation Based on Orthogonality Conditions 508
13.4.2 Generalizing the Method of Moments 510
13.4.3 Properties of the GMM Estimator 514
13.5 Testing Hypotheses in the GMM Framework 519
13.5.1 Testing the Validity of the Moment Restrictions 519
13.5.2 GMM Counterparts to the WALD, LM, and LR
Tests 520
14 Contents
13.6 GMM Estimation of Econometric Models 522
13.6.1 Single-Equation Linear Models 522
13.6.2 Single-Equation Nonlinear Models 528
13.6.3 Seemingly Unrelated Regression Models 531
13.6.4 Simultaneous Equations Models with Heteroscedasticity 533
13.6.5 GMM Estimation of Dynamic Panel Data Models 536
13.7 Summary and Conclusions 547
CHAPTER 14 Maximum Likelihood Estimation 549
14.1 Introduction 549
14.2 The Likelihood Function and Identification of the Parameters 549
14.3 Efficient Estimation: The Principle of Maximum Likelihood 551
14.4 Properties of Maximum Likelihood Estimators 553
14.4.1 Regularity Conditions 554
14.4.2 Properties of Regular Densities 555
14.4.3 The Likelihood Equation 557
14.4.4 The Information Matrix Equality 557
14.4.5 Asymptotic Properties of the Maximum Likelihood
Estimator 557
14.4.5.a Consistency 558
14.4.5.b Asymptotic Normality 559
14.4.5.c Asymptotic Efficiency 560
14.4.5.d Invariance 561
14.4.5.e Conclusion 561
14.4.6 Estimating the Asymptotic Variance of the Maximum
Likelihood Estimator 561
14.5 Conditional Likelihoods, Econometric Models, and the GMM
Estimator 563
14.6 Hypothesis and Specification Tests and Fit Measures 564
14.6.1 The Likelihood Ratio Test 566
14.6.2 The Wald Test 567
14.6.3 The Lagrange Multiplier Test 569
14.6.4 An Application of the Likelihood-Based Test Procedures 571
14.6.5 Comparing Models and Computing Model Fit 573
14.6.6 Vuong s Test and the Kullback-Leibler Information
Criterion 574
14.7 Two-Step Maximum Likelihood Estimation 576
14.8 Pseudo-Maximum Likelihood Estimation and Robust Asymptotic
Covariance Matrices 582
14.8.1 Maximum Likelihood and GMM Estimation 583
14.8.2 Maximum Likelihood and M Estimation 583
14.8.3 Sandwich Estimators 585
14.8.4 Cluster Estimators 586
Contents 15
14.9 Applications of Maximum Likelihood Estimation 588
14.9.1 The Normal Linear Regression Model 588
14.9.2 The Generalized Regression Model 592
14.9.2.a Multiplicative Heteroscedasticity 594
14.9.2.b Autocorrelation 597
14.9.3 Seemingly Unrelated Regression Models 600
14.9.3.a The Pooled Model 600
14.9.3.b The SUR Model 602
14.9.3.c Exclusion Restrictions 602
14.9.4 Simultaneous Equations Models 607
14.9.5 Maximum Likelihood Estimation of Nonlinear Regression
Models 608
14.9.6 Panel Data Applications 613
14.9.6.a ML Estimation of the Linear Random Effects
Model 614
14.9.6.b Nested Random Effects 616
14.9.6.C Random Effects in Nonlinear Models: MLE Using
Quadrature 620
14.9.6A Fixed Effects in Nonlinear Models: Full MLE 624
14.10 Latent Class and Finite Mixture Models 628
14.10.1 A Finite Mixture Model 629
14.10.2 Measured and Unmeasured Heterogeneity 631
14.10.3 Predicting Class Membership 631
14.10.4 A Conditional Latent Class Model 632
14.10.5 Determining the Number of Classes 634
14.10.6 A Panel Data Application 635
14.11 Summary and Conclusions 638
CHAPTER 15 Simulation-Based Estimation and Inference and Random Parameter
Models 643
15.1 Introduction 643
15.2 Random Number Generation 645
15.2.1 Generating Pseudo-Random Numbers 645
15.2.2 Sampling from a Standard Uniform Population 646
15.2.3 Sampling from Continuous Distributions 647
15.2.4 Sampling from a Multivariate Normal Population 648
15.2.5 Sampling from Discrete Populations 648
15.3 Simulation-Based Statistical Inference: The Method of Krinsky and
Robb 649
15.4 Bootstrapping Standard Errors and Confidence Intervals 651
15.5 Monte Carlo Studies 655
15.5.1 A Monte Carlo Study: Behavior of a Test Statistic 657
15.5.2 A Monte Carlo Study: The Incidental Parameters Problem 659
15.6 Simulation-Based Estimation 661
15.6.1 Random Effects in a Nonlinear Model 661
16 Contents
15.6.2 Monte Carlo Integration 663
15.6.2. a Halton Sequences and Random Draws for
Simulation-Based Integration 665
15.6.2.b Computing Multivariate Normal Probabilities Using
the GHK Simulator 667
15.6.3 Simulation-Based Estimation of Random Effects Models 669
15.7 A Random Parameters Linear Regression Model 674
15.8 Hierarchical Linear Models 679
15.9 Nonlinear Random Parameter Models 681
15.10 Individual Parameter Estimates 682
15.11 Mixed Models and Latent Class Models 690
15.12 Summary and Conclusions 693
CHAPTER 16 Bayesian Estimation and Inference 695
16.1 Introduction 695
16.2 Bayes Theorem and the Posterior Density 696
16.3 Bayesian Analysis of the Classical Regression Model 698
16.3.1 Analysis with a Noninformative Prior 699
16.3.2 Estimation with an Informative Prior Density 701
16.4 Bayesian Inference 704
16.4.1 Point Estimation 704
16.4.2 Interval Estimation 705
16.4.3 Hypothesis Testing 706
16.4.4 Large-Sample Results 708
16.5 Posterior Distributions and the Gibbs Sampler 708
16.6 Application: Binomial Probit Model 711
16.7 Panel Data Application: Individual Effects Models 714
16.8 Hierarchical Bayes Estimation of a Random Parameters Model 716
16.9 Summary and Conclusions 718
PART IV Cross Sections, Panel Data, and Microeconometrics
CHAPTER 17 Discrete Choice 721
17.1 Introduction 721
17.2 Models for Binary Outcomes 723
17.2.1 Random Utility Models for Individual Choice 724
17.2.2 A Latent Regression Model 726
17.2.3 Functional Form and Regression 727
17.3 Estimation and Inference in Binary Choice Models 730
17.3.1 Robust Covariance Matrix Estimation 732
17.3.2 Marginal Effects and Average Partial Effects 733
Contents 17
17.3.2.a Average Partid Effects 736
17.3.2. b Interaction Effects 739
17.3.3 Measuring Goodness of Fit 741
17.3.4 Hypothesis Tests 743
17.3.5 Endogenous Right-Hand-Side Variables in Binary Choice
Models 746
17.3.6 Endogenous Choice-Based Sampling 750
17.3.7 Specification Analysis 751
17.3.7.a Omitted Variables 753
17.3.7.b Heteroscedasticity 754
17 A Binary Choice Models for Panel Data 756
17.4.1 The Pooled Estimator 757
17.4.2 Random Effects Models 758
17.4.3 Fixed Effects Models 761
17.4.4 A Conditional Fixed Effects Estimator 762
17.4.5 Mundlak s Approach, Variable Addition, and Bias
Reduction 767
17.4.6 Dynamic Binary Choice Models 769
17.4.7 A Semiparametric Model for Individual Heterogeneity 771
17.4.8 Modeling Parameter Heterogeneity 773
17.4.9 Nonresponse, Attrition, and Inverse Probability Weighting 774
17.5 Bivariate and Multivariate Probit Models 778
17.5.1 Maximum Likelihood Estimation 779
17.5.2 Testing for Zero Correlation 782
17.5.3 Partial Effects 782
17.5.4 A Panel Data Model for Bivariate Binary Response 784
17.5.5 Endogenous Binary Variable in a Recursive Bivariate Probit
Model 785
17.5.6 Endogenous Sampling in a Binary Choice Model 789
17.5.7 A Multivariate Probit Model 792
17.6 Summary and Conclusions 795
CHAPTER 18 Discrete Choices and Event Counts 800
18.1 Introduction 800
18.2 Models for Unordered Multiple Choices 801
18.2.1 Random Utility Basis of the Multinomial Logit Model 801
18.2.2 The Multinomial Logit Model 803
18.2.3 The Conditional Logit Model 806
18.2.4 The Independence from Irrelevant Alternatives
Assumption 807
18.2.5 Nested Logit Models 808
18.2.6 The Multinomial Probit Model 810
18.2.7 The Mixed Logit Model 811
18.2.8 A Generalized Mixed Logit Model 812
18 Contents
18.2.9 Application: Conditional Logit Model for Travel Mode
Choice 813
18.2.10 Estimating Willingness to Pay 819
18.2.11 Panel Data and Stated Choice Experiments 821
18.2.12 Aggregate Market Share Data— The BLP Random Parameters
Model 822
18.3 Random Utility Models for Ordered Choices 824
18.3.1 The Ordered Probit Model 827
18.3.2 A Specification Test for the Ordered Choice Model 831
18.3.3 Bivariate Ordered Probit Models 832
18.3.4 Panel Data Applications 834
18.3.4.a Ordered Probit Models with Fixed Effects 834
18.3.4.b Ordered Probit Models with Random Effects 835
18.3.5 Extensions of the Ordered Probit Model 838
18.3.5.a Threshold Models— Generalized Ordered Choice
Models 839
18.3.5.b Thresholds and Heterogeneity—Anchoring
Vignettes 840
18.4 Models for Counts of Events 842
18.4.1 The Poisson Regression Model 843
18.4.2 Measuring Goodness of Fit 844
18.4.3 Testing for Overdispersion 845
18.4.4 Heterogeneity and the Negative Binomial Regression
Model 846
18.4.5 Functional Forms for Count Data Models 847
18.4.6 Truncation and Censoring in Models for Counts 850
18.4.7 Panel Data Models 855
18.4.7.a Robust Covariance Matrices for Pooled
Estimators 856
18.4.7.b Fixed Effects 857
18.4.7.c Random Effects 858
18.4.8 Two-Part Models: Zero Inflation and Hurdle Models 861
18.4.9 Endogenous Variables and Endogenous Participation 866
18.5 Summary and Conclusions 869
CHAPTER 19 Limited Dependent Variables—Truncation, Censoring, and Sample
Selection 873
19.1 Introduction 873
19.2 Truncation 873
19.2.1 Truncated Distributions 874
19.2.2 Moments of Truncated Distributions 875
19.2.3 The Truncated Regression Model 877
19.2.4 The Stochastic Frontier Model 879
19.3 Censored Data 885
19.3.1 The Censored Normal Distribution 886
Contents 19
19.3.2 The Censored Regression (Tobit) Model 888
19.3.3 Estimation 890
19.3.4 Two-Part Models and Corner Solutions 892
19.3.5 Some Issues in Specification 898
19.3.5.a Heteroscedasticity 898
19.3.5.Ò Nonnormality 899
19.3.6 Panel Data Applications 900
19.4 Models for Duration 901
19.4.1 Models for Duration Data 902
19.4.2 Duration Data 902
19.4.3 A Regression-Like Approach: Parametric Models of
Duration 903
19.4.3.a Theoretical Background 903
19.4.3.b Models of the Hazard Function 904
19.4.3.c Maximum Likelihood Estimation 906
19.4.3.d Exogenous Variables 907
19.4.3.e Heterogeneity 908
19.4.4 Nonparametric and Semiparametric Approaches 909
19.5 Incidental Truncation and Sample Selection 912
19.5.1 Incidental Truncation in a Bivariate Distribution 913
19.5.2 Regression in a Model of Selection 913
19.5.3 Two-Step and Maximum Likelihood Estimation 916
19.5.4 Sample Selection in Nonlinear Models 920
19.5.5 Panel Data Applications of Sample Selection Models 923
19.5.5.a Common Effects in Sample Selection Models 924
19.5.5.b Attrition 926
19.6 Evaluating Treatment Effects 928
19.6.1 Regression Analysis of Treatment Effects 930
19.6.1.a The Normality Assumption 932
19.6.1.b Estimating the Effect of Treatment on
the Treated 933
19.6.2 Propensity Score Matching 934
19.6.3 Regression Discontinuity 937
19.7 Summary and Conclusions 938
PART V Time Series and Macroeconometrics
CHAPTER 20 Serial Correlation 943
20.1 Introduction 943
20.2 The Analysis of Time-Series Data 946
20.3 Disturbance Processes 949
20.3.1 Characteristics of Disturbance Processes 949
20.3.2 AR(1) Disturbances 950
20 Contents
20.4 Some Asymptotic Results for Analyzing Time-Series Data 952
20.4.1 Convergence ojMoments—The Ergodic Theorem 953
20.4.2 Convergence to Normality—A Central Limit Theorem 955
20.5 Least Squares Estimation 958
20.5.1 Asymptotic Properties of Least Squares 958
20.5.2 Estimating the Variance of the Least Squares Estimator 959
20.6 GMM Estimation 961
20.7 Testing for Autocorrelation 962
20.7.1 Lagrange Multiplier Test 962
20.7.2 Box and Pierce s Test and Ljung s Refinement 962
20.7.3 The Durbin-Watson Test 963
20.7.4 Testing in the Presence of a Lagged Dependent
Variable 963
20.7.5 Summary of Testing Procedures 964
20.8 Efficient Estimation When ß Is Known 964
20.9 Estimation When fl Is Unknown 966
20.9.1 AR(1) Disturbances 966
20.9.2 Application: Estimation of a Model with Autocorrelation 967
20.9.3 Estimation with a Lagged Dependent Variable 969
20.10 Autoregressive Conditional Heteroscedasticity 970
20.10.1 The ARCH(l) Model 971
20.10.2 ARCH(q), ARCH-in-Mean, and Generalized ARCH
Models 972
20.10.3 Maximum Likelihood Estimation of the Garch Model 974
20.10.4 Testing for Garch Effects 976
20.10.5 Pseudo-Maximum Likelihood Estimation 977
20.11 Summary and Conclusions 979
CHAPTER 21 Nonstationary Data 982
21.1 Introduction 982
21.2 Nonstationary Processes and Unit Roots 982
21.2.1 Integrated Processes and Differencing 982
21.2.2 Random Walks, Trends, and Spurious Regressions 984
21.2.3 Tests for Unit Roots in Economic Data 987
21.2.4 The Dickey-Fuller Tests 988
21.2.5 The KPSS Test of Stationarity 998
21.3 Cointegration 999
21.3.1 Common Trends 1002
21.3.2 Error Correction and VAR Representations 1003
21.3.3 Testing for Cointegration 1005
21.3.4 Estimating Cointegration Relationships 1007
21.3.5 Application: German Money Demand 1007
21.3.5.a Cointegration Analysis and a Long-Run Theoretical
Model 1008
21.3.5.b Testing for Model Instability 1009
Contents 21
21.4 Nonstationary Panel Data 1010
21.5 Summary and Conclusions 1011
PART VI Appendices
Appendix A Matrix Algebra 1013
A.I Terminology 1013
A.2 Algebraic Manipulation of Matrices 1013
A.2.1 Equality of Matrices 1013
A.2.2 Transposition 1014
A.2.3 Matrix Addition 1014
A.2.4 Vector Multiplication 1015
A.2.5 A Notation for Rows and Columns of a Matrix 1015
A.2.6 Matrix Multiplication and Scalar Multiplication 1015
A.2.7 Sums of Values 1017
A.2.8 A Useful Idempotent Matrix 1018
A.3 Geometry of Matrices 1019
A.3.1 Vector Spaces 1019
A.3.2 Linear Combinations of Vectors and Basis Vectors 1021
A.3.3 Linear Dependence 1022
A.3.4 Subspaces 1023
A.3.5 Rank of a Matrix 1024
A.3.6 Determinant of a Matrix 1026
A.3.7 A Least Squares Problem 1027
A.4 Solution of a System of Linear Equations 1029
A.4.1 Systems of Linear Equations 1029
A.4.2 Inverse Matrices 1030
A.4.3 Nonhomogeneous Systems of Equations 1032
A.4.4 Solving the Least Squares Problem 1032
A.5 Partitioned Matrices 1032
A.5.1 Addition and Multiplication of Partitioned Matrices 1033
A.5.2 Determinants of Partitioned Matrices 1033
A.5.3 Inverses of Partitioned Matrices 1033
A.5.4 Deviations from Means 1034
A.5.5 Kronecker Products 1034
A.6 Characteristic Roots and Vectors 1035
A.6.1 The Characteristic Equation 1035
A.6.2 Characteristic Vectors 1036
A.6.3 General Results for Characteristic Roots and Vectors 1036
A.6.4 Diagonalization and Spectral Decomposition of a Matrix 1037
A.6.5 Rank of a Matrix 1037
A.6.6 Condition Number of a Matrix 1039
A.6.7 Trace of a Matrix 1039
A.6.8 Determinant of a Matrix 1040
A.6.9 Powers of a Matrix 1040
A.6.10 Idempotent Matrices 1042
22 Contents
A.6.11 Factoring a Matrix 1042
A.6.12 The Generalized Inverse of a Matrix 1043
A.7 Quadratic Forms and Definite Matrices 1044
A.7.1 Nonnegative Definite Matrices 1045
A.7.2 Idempotent Quadratic Forms 1046
A.7.3 Comparing Matrices 1046
A.8 Calculus and Matrix Algebra 1047
A.8.1 Differentiation and the Taylor Series 1047
A.8.2 Optimization 1050
A.8.3 Constrained Optimization 1052
A.8.4 Transformations 1054
Appendix B Probability and Distribution Theory 1055
B.1 Introduction 1055
B.2 Random Variables 1055
B.2.1 Probability Distributions 1055
B.2.2 Cumulative Distribution Function 1056
B.3 Expectations of a Random Variable 1057
B.4 Some Specific Probability Distributions 1059
B.4.1 The Normal Distribution 1059
B.4.2 The Chi-Squared, t, and F Distributions 1061
B.4.3 Distributions with Large Degrees of Freedom 1063
B.4.4 Size Distributions: The Lognormal Distribution 1064
B.4.5 The Gamma and Exponential Distributions 1064
B.4.6 The Beta Distribution 1065
B.4.7 The Logistic Distribution 1065
B.4.8 The Wishart Distribution 1065
B.4.9 Discrete Random Variables 1066
B.5 The Distribution of a Function of a Random Variable 1066
B.6 Representations of a Probability Distribution 1068
B.7 Joint Distributions 1070
B.7.1 Marginal Distributions 1070
B.7.2 Expectations in a Joint Distribution 1071
B.7.3 Covariance and Correlation 1071
B.7.4 Distribution of a Function ofBivariate Random
Variables 1072
B.8 Conditioning in a Bivariate Distribution 1074
B.8.1 Regression: The Conditional Mean 1074
B.8.2 Conditional Variance 1075
B.8.3 Relationships Among Marginal and Conditional
Moments 1075
B.8.4 The Analysis of Variance 1077
B.9 The Bivariate Normal Distribution 1077
B.10 Multivariate Distributions 1078
B.10.1 Moments 1078
Contents 23
B.10.2 Sets of Linear Functions 1079
B.10.3 Nonlinear Functions 1080
B.ll The Multivariate Normal Distribution 1081
B.ll.l Marginal and Conditional Normal Distributions 1081
B.11.2 The Classical Normal Linear Regression Model 1082
B.11.3 Linear Functions of a Normal Vector 1083
B.11.4 Quadratic forms in a Standard Normal Vector 1083
B.11.5 The F Distribution 1085
B.11.6 A Full Rank Quadratic Form 1085
B.I 1.7 Independence of a Linear and a Quadratic Form 1086
Appendix C Estimation and Inference 1087
C.1 Introduction 1087
C.2 Samples and Random Sampling 1088
C.3 Descriptive Statistics 1088
C.4 Statistics as Estimators—Sampling Distributions 1091
C.5 Point Estimation of Parameters 1095
C.5.1 Estimation in a Finite Sample 1095
C.5.2 Efficient Unbiased Estimation 1098
C.6 Interval Estimation 1100
C.7 Hypothesis Testing 1102
C.7.1 Classical Testing Procedures 1102
C.7.2 Tests Based on Confidence Intervals 1105
C.7.3 Specification Tests 1106
Appendix D Large-Sample Distribution Theory 1106
D.I Introduction 1106
D.2 Large-Sample Distribution Theory 1107
D.2.1 Convergence in Probability 1107
D.2.2 Other forms of Convergence and Laws of Large
Numbers 1110
D.2.3 Convergence of Functions 1113
D.2.4 Convergence to a Random Variable 1114
D.2.5 Convergence in Distribution: Limiting Distributions 1116
D.2.6 Central Limit Theorems 1118
D.2.7 The Delta Method 1123
D.3 Asymptotic Distributions 1124
D.3.1 Asymptotic Distribution of a Nonlinear Function 1126
D.3.2 Asymptotic Expectations 1127
D.4 Sequences and the Order of a Sequence 1128
Appendix £ Computation and Optimization 1129
E.I Introduction 1129
E.2 Computation in Econometrics 1130
E.2.1 Computing Integrals 1130
24 Contents
E.2.2 The Standard Normal Cumulative Distribution Function 1130
E.2.3 The Gamma and Related Functions 1131
E.2.4 Approximating Integrals by Quadrature 1132
E.3 Optimization 1133
E.3.1 Algorithms 1135
E.3.2 Computing Derivatives 1136
E.3.3 Gradient Methods 1137
E.3.4 Aspects of Maximum Likelihood Estimation 1140
E.3.5 Optimization with Constraints 1141
E.3.6 Some Practical Considerations 1142
E.3.7 The EM Algorithm 1144
E.4 Examples 1146
E.4.1 Function of One Parameter 1146
E.4.2 Function of Two Parameters: The Gamma Distribution 1147
E.4.3 A Concentrated Log-Likelihood Function 1148
Appendix F Data Sets Used in Applications 1149
References 1155
Combined Author and Subject Index 1201
|
| any_adam_object | 1 |
| author | Greene, William 1951- |
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| author_variant | w g wg |
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| callnumber-first | H - Social Science |
| callnumber-label | HB139 |
| callnumber-raw | HB139 |
| callnumber-search | HB139 |
| callnumber-sort | HB 3139 |
| callnumber-subject | HB - Economic Theory and Demography |
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| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/5195 |
| dewey-search | 330/.01/5195 |
| dewey-sort | 3330 11 45195 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 7. ed., internat. ed. |
| format | Book |
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| genre_facet | Lehrbuch |
| id | DE-604.BV037347726 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T06:42:44Z |
| institution | BVB |
| isbn | 9780273753568 0273753568 9780131395381 |
| language | English |
| lccn | 2010050532 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022501276 |
| oclc_num | 734051195 |
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| physical | 1238 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
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| publisher | Pearson |
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| spellingShingle | Greene, William 1951- Econometric analysis Econometria larpcal Econometrie gtt Économétrie Econometrics Mathematische Methode (DE-588)4155620-3 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4155620-3 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Econometric analysis |
| title_auth | Econometric analysis |
| title_exact_search | Econometric analysis |
| title_full | Econometric analysis William H. Greene |
| title_fullStr | Econometric analysis William H. Greene |
| title_full_unstemmed | Econometric analysis William H. Greene |
| title_short | Econometric analysis |
| title_sort | econometric analysis |
| topic | Econometria larpcal Econometrie gtt Économétrie Econometrics Mathematische Methode (DE-588)4155620-3 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometria Econometrie Économétrie Econometrics Mathematische Methode Ökonometrie Lehrbuch |
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