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Microeconometrics
Methods and Applications
Hardcover | 16 August 2005
At a Glance
1058 Pages
25.6 x 18.3 x 3.9
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| List of Figures | p. xv |
| List of Tables | p. xvii |
| Preface | p. xxi |
| Preliminaries | |
| Overview | p. 3 |
| Introduction | p. 3 |
| Distinctive Aspects of Microeconometrics | p. 5 |
| Book Outline | p. 10 |
| How to Use This Book | p. 14 |
| Software | p. 15 |
| Notation and Conventions | p. 16 |
| Causal and Noncausal Models | p. 18 |
| Introduction | p. 18 |
| Structural Models | p. 20 |
| Exogeneity | p. 22 |
| Linear Simultaneous Equations Model | p. 23 |
| Identification Concepts | p. 29 |
| Single-Equation Models | p. 31 |
| Potential Outcome Model | p. 31 |
| Causal Modeling and Estimation Strategies | p. 35 |
| Bibliographic Notes | p. 38 |
| Microeconomic Data Structures | p. 39 |
| Introduction | p. 39 |
| Observational Data | p. 40 |
| Data from Social Experiments | p. 48 |
| Data from Natural Experiments | p. 54 |
| Practical Considerations | p. 58 |
| Bibliographic Notes | p. 61 |
| Core Methods | |
| Linear Models | p. 65 |
| Introduction | p. 65 |
| Regressions and Loss Functions | p. 66 |
| Example: Returns to Schooling | p. 69 |
| Ordinary Least Squares | p. 70 |
| Weighted Least Squares | p. 81 |
| Median and Quantile Regression | p. 85 |
| Model Misspecification | p. 90 |
| Instrumental Variables | p. 95 |
| Instrumental Variables in Practice | p. 103 |
| Practical Considerations | p. 112 |
| Bibliographic Notes | p. 112 |
| Maximum Likelihood and Nonlinear Least-Squares Estimation | p. 116 |
| Introduction | p. 116 |
| Overview of Nonlinear Estimators | p. 117 |
| Extremum Estimators | p. 124 |
| Estimating Equations | p. 133 |
| Statistical Inference | p. 135 |
| Maximum Likelihood | p. 139 |
| Quasi-Maximum Likelihood | p. 146 |
| Nonlinear Least Squares | p. 150 |
| Example: ML and NLS Estimation | p. 159 |
| Practical Considerations | p. 163 |
| Bibliographic Notes | p. 163 |
| Generalized Method of Moments and Systems Estimation | p. 166 |
| Introduction | p. 166 |
| Examples | p. 167 |
| Generalized Method of Moments | p. 172 |
| Linear Instrumental Variables | p. 183 |
| Nonlinear Instrumental Variables | p. 192 |
| Sequential Two-Step m-Estimation | p. 200 |
| Minimum Distance Estimation | p. 202 |
| Empirical Likelihood | p. 203 |
| Linear Systems of Equations | p. 206 |
| Nonlinear Sets of Equations | p. 214 |
| Practical Considerations | p. 219 |
| Bibliographic Notes | p. 220 |
| Hypothesis Tests | p. 223 |
| Introduction | p. 223 |
| Wald Test | p. 224 |
| Likelihood-Based Tests | p. 233 |
| Example: Likelihood-Based Hypothesis Tests | p. 241 |
| Tests in Non-ML Settings | p. 243 |
| Power and Size of Tests | p. 246 |
| Monte Carlo Studies | p. 250 |
| Bootstrap Example | p. 254 |
| Practical Considerations | p. 256 |
| Bibliographic Notes | p. 257 |
| Specification Tests and Model Selection | p. 259 |
| Introduction | p. 259 |
| m-Tests | p. 260 |
| Hausman Test | p. 271 |
| Tests for Some Common Misspecifications | p. 274 |
| Discriminating between Nonnested Models | p. 278 |
| Consequences of Testing | p. 285 |
| Model Diagnostics | p. 287 |
| Practical Considerations | p. 291 |
| Bibliographic Notes | p. 292 |
| Semiparametric Methods | p. 294 |
| Introduction | p. 294 |
| Nonparametric Example: Hourly Wage | p. 295 |
| Kernel Density Estimation | p. 298 |
| Nonparametric Local Regression | p. 307 |
| Kernel Regression | p. 311 |
| Alternative Nonparametric Regression Estimators | p. 319 |
| Semiparametric Regression | p. 322 |
| Derivations of Mean and Variance of Kernel Estimators | p. 330 |
| Practical Considerations | p. 333 |
| Bibliographic Notes | p. 333 |
| Numerical Optimization | p. 336 |
| Introduction | p. 336 |
| General Considerations | p. 336 |
| Specific Methods | p. 341 |
| Practical Considerations | p. 348 |
| Bibliographic Notes | p. 352 |
| Simulation-Based Methods | |
| Bootstrap Methods | p. 357 |
| Introduction | p. 357 |
| Bootstrap Summary | p. 358 |
| Bootstrap Example | p. 366 |
| Bootstrap Theory | p. 368 |
| Bootstrap Extensions | p. 373 |
| Bootstrap Applications | p. 376 |
| Practical Considerations | p. 382 |
| Bibliographic Notes | p. 382 |
| Simulation-Based Methods | p. 384 |
| Introduction | p. 384 |
| Examples | p. 385 |
| Basics of Computing Integrals | p. 387 |
| Maximum Simulated Likelihood Estimation | p. 393 |
| Moment-Based Simulation Estimation | p. 398 |
| Indirect Inference | p. 404 |
| Simulators | p. 406 |
| Methods of Drawing Random Variates | p. 410 |
| Bibliographic Notes | p. 416 |
| Bayesian Methods | p. 419 |
| Introduction | p. 419 |
| Bayesian Approach | p. 420 |
| Bayesian Analysis of Linear Regression | p. 435 |
| Monte Carlo Integration | p. 443 |
| Markov Chain Monte Carlo Simulation | p. 445 |
| MCMC Example: Gibbs Sampler for SUR | p. 452 |
| Data Augmentation | p. 454 |
| Bayesian Model Selection | p. 456 |
| Practical Considerations | p. 458 |
| Bibliographic Notes | p. 458 |
| Models for Cross-Section Data | |
| Binary Outcome Models | p. 463 |
| Introduction | p. 463 |
| Binary Outcome Example: Fishing Mode Choice | p. 464 |
| Logit and Probit Models | p. 465 |
| Latent Variable Models | p. 475 |
| Choice-Based Samples | p. 478 |
| Grouped and Aggregate Data | p. 480 |
| Semiparametric Estimation | p. 482 |
| Derivation of Logit from Type I Extreme Value | p. 486 |
| Practical Considerations | p. 487 |
| Bibliographic Notes | p. 487 |
| Multinomial Models | p. 490 |
| Introduction | p. 490 |
| Example: Choice of Fishing Mode | p. 491 |
| General Results | p. 495 |
| Multinomial Logit | p. 500 |
| Additive Random Utility Models | p. 504 |
| Nested Logit | p. 507 |
| Random Parameters Logit | p. 512 |
| Multinomial Probit | p. 516 |
| Ordered, Sequential, and Ranked Outcomes | p. 519 |
| Multivariate Discrete Outcomes | p. 521 |
| Semiparametric Estimation | p. 523 |
| Derivations for MNL, CL, and NL Models | p. 524 |
| Practical Considerations | p. 527 |
| Bibliographic Notes | p. 528 |
| Tobit and Selection Models | p. 529 |
| Introduction | p. 529 |
| Censored and Truncated Models | p. 530 |
| Tobit Model | p. 536 |
| Two-Part Model | p. 544 |
| Sample Selection Models | p. 546 |
| Selection Example: Health Expenditures | p. 553 |
| Roy Model | p. 555 |
| Structural Models | p. 558 |
| Semiparametric Estimation | p. 562 |
| Derivations for the Tobit Model | p. 566 |
| Practical Considerations | p. 568 |
| Bibliographic Notes | p. 569 |
| Transition Data: Survival Analysis | p. 573 |
| Introduction | p. 573 |
| Example: Duration of Strikes | p. 574 |
| Basic Concepts | p. 576 |
| Censoring | p. 579 |
| Nonparametric Models | p. 580 |
| Parametric Regression Models | p. 584 |
| Some Important Duration Models | p. 591 |
| Cox PH Model | p. 592 |
| Time-Varying Regressors | p. 597 |
| Discrete-Time Proportional Hazards | p. 600 |
| Duration Example: Unemployment Duration | p. 603 |
| Practical Considerations | p. 608 |
| Bibliographic Notes | p. 608 |
| Mixture Models and Unobserved Heterogeneity | p. 611 |
| Introduction | p. 611 |
| Unobserved Heterogeneity and Dispersion | p. 612 |
| Identification in Mixture Models | p. 618 |
| Specification of the Heterogeneity Distribution | p. 620 |
| Discrete Heterogeneity and Latent Class Analysis | p. 621 |
| Stock and Flow Sampling | p. 625 |
| Specification Testing | p. 628 |
| Unobserved Heterogeneity Example: Unemployment Duration | p. 632 |
| Practical Considerations | p. 637 |
| Bibliographic Notes | p. 637 |
| Models of Multiple Hazards | p. 640 |
| Introduction | p. 640 |
| Competing Risks | p. 642 |
| Joint Duration Distributions | p. 648 |
| Multiple Spells | p. 655 |
| Competing Risks Example: Unemployment Duration | p. 658 |
| Practical Considerations | p. 662 |
| Bibliographic Notes | p. 663 |
| Models of Count Data | p. 665 |
| Introduction | p. 665 |
| Basic Count Data Regression | p. 666 |
| Count Example: Contacts with Medical Doctor | p. 671 |
| Parametric Count Regression Models | p. 674 |
| Partially Parametric Models | p. 682 |
| Multivariate Counts and Endogenous Regressors | p. 685 |
| Count Example: Further Analysis | p. 690 |
| Practical Considerations | p. 690 |
| Bibliographic Notes | p. 691 |
| Models for Panel Data | |
| Linear Panel Models: Basics | p. 697 |
| Introduction | p. 697 |
| Overview of Models and Estimators | p. 698 |
| Linear Panel Example: Hours and Wages | p. 708 |
| Fixed Effects versus Random Effects Models | p. 715 |
| Pooled Models | p. 720 |
| Fixed Effects Model | p. 726 |
| Random Effects Model | p. 734 |
| Modeling Issues | p. 737 |
| Practical Considerations | p. 740 |
| Bibliographic Notes | p. 740 |
| Linear Panel Models: Extensions | p. 743 |
| Introduction | p. 743 |
| GMM Estimation of Linear Panel Models | p. 744 |
| Panel GMM Example: Hours and Wages | p. 754 |
| Random and Fixed Effects Panel GMM | p. 756 |
| Dynamic Models | p. 763 |
| Difference-in-Differences Estimator | p. 768 |
| Repeated Cross Sections and Pseudo Panels | p. 770 |
| Mixed Linear Models | p. 774 |
| Practical Considerations | p. 776 |
| Bibliographic Notes | p. 777 |
| Nonlinear Panel Models | p. 779 |
| Introduction | p. 779 |
| General Results | p. 779 |
| Nonlinear Panel Example: Patents and R&D | p. 762 |
| Binary Outcome Data | p. 795 |
| Tobit and Selection Models | p. 800 |
| Transition Data | p. 801 |
| Count Data | p. 802 |
| Semiparametric Estimation | p. 808 |
| Practical Considerations | p. 808 |
| Bibliographic Notes | p. 809 |
| Further Topics | |
| Stratified and Clustered Samples | p. 813 |
| Introduction | p. 813 |
| Survey Sampling | p. 814 |
| Weighting | p. 817 |
| Endogenous Stratification | p. 822 |
| Clustering | p. 829 |
| Hierarchical Linear Models | p. 845 |
| Clustering Example: Vietnam Health Care Use | p. 848 |
| Complex Surveys | p. 853 |
| Practical Considerations | p. 857 |
| Bibliographic Notes | p. 857 |
| Treatment Evaluation | p. 860 |
| Introduction | p. 860 |
| Setup and Assumptions | p. 862 |
| Treatment Effects and Selection Bias | p. 865 |
| Matching and Propensity Score Estimators | p. 871 |
| Differences-in-Differences Estimators | p. 878 |
| Regression Discontinuity Design | p. 879 |
| Instrumental Variable Methods | p. 883 |
| Example: The Effect of Training on Earnings | p. 889 |
| Bibliographic Notes | p. 896 |
| Measurement Error Models | p. 899 |
| Introduction | p. 899 |
| Measurement Error in Linear Regression | p. 900 |
| Identification Strategies | p. 905 |
| Measurement Errors in Nonlinear Models | p. 911 |
| Attenuation Bias Simulation Examples | p. 919 |
| Bibliographic Notes | p. 920 |
| Missing Data and Imputation | p. 923 |
| Introduction | p. 923 |
| Missing Data Assumptions | p. 925 |
| Handling Missing Data without Models | p. 928 |
| Observed-Data Likelihood | p. 929 |
| Regression-Based Imputation | p. 930 |
| Data Augmentation and MCMC | p. 932 |
| Multiple Imputation | p. 934 |
| Missing Data MCMC Imputation Example | p. 935 |
| Practical Considerations | p. 939 |
| Bibliographic Notes | p. 940 |
| Asymptotic Theory | p. 943 |
| Introduction | p. 943 |
| Convergence in Probability | p. 944 |
| Laws of Large Numbers | p. 947 |
| Convergence in Distribution | p. 948 |
| Central Limit Theorems | p. 949 |
| Multivariate Normal Limit Distributions | p. 951 |
| Stochastic Order of Magnitude | p. 954 |
| Other Results | p. 955 |
| Bibliographic Notes | p. 956 |
| Making Pseudo-Random Draws | p. 957 |
| References | p. 961 |
| Index | p. 999 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780521848053
ISBN-10: 0521848059
Published: 16th August 2005
Format: Hardcover
Language: English
Number of Pages: 1058
Audience: Professional and Scholarly
Publisher: Cambridge University Press
Country of Publication: GB
Dimensions (cm): 25.6 x 18.3 x 3.9
Weight (kg): 1.81
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