Microeconometrics : methods and applications / A. Colin Cameron, Pravin K. Trivedi.

Format:

Book

Author/Creator:

Cameron, A. Colin (Adrian Colin)

Contributor:

Trivedi, P. K.

Charles R. Anderson Endowment Fund.

Language:

English

Subjects (All):

Microeconomics--Econometric models.

Microeconomics.

Physical Description:

xxii, 1034 pages : illustrations ; 27 cm

Place of Publication:

Cambridge ; New York : Cambridge University Press, 2005.

Summary:

This book provides a comprehensive treatment of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods applied to cross section and panel data. The book is oriented to the practitioner. A good understanding of the linear regression model with matrix algebra is assumed. The text can be used for Ph.D. courses in microeconometrics, in applied econometrics, or in data-oriented microeconomics sub-disciplines; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their tool kit. Distinguishing features include emphasis on nonlinear models and robust inference, as well as chapter-length treatments of GMM estimation, nonparametric regression, simulation-based estimation, bootstrap methods, Bayesian methods, stratified and clustered samples, treatment evaluation, measurementerror, and missing data. The book makes frequent use of empirical illustrations, many based on seven large and rich data sets.

Contents:

1.2 Distinctive Aspects of Microeconometrics 5

1.3 Book Outline 10

1.5 Software 15

1.6 Notation and Conventions 16

2 Causal and Noncausal Models 18

2.2 Structural Models 20

2.3 Exogeneity 22

2.4 Linear Simultaneous Equations Model 23

2.5 Identification Concepts 29

2.6 Single-Equation Models 31

2.7 Potential Outcome Model 31

2.8 Causal Modeling and Estimation Strategies 35

3 Microeconomic Data Structures 39

3.2 Observational Data 40

3.3 Data from Social Experiments 48

3.4 Data from Natural Experiments 54

II Core Methods

4 Linear Models 65

4.2 Regressions and Loss Functions 66

4.3 Example: Returns to Schooling 69

4.4 Ordinary Least Squares 70

4.5 Weighted Least Squares 81

4.6 Median and Quantile Regression 85

4.7 Model Misspecification 90

4.8 Instrumental Variables 95

4.9 Instrumental Variables in Practice 103

5 Maximum Likelihood and Nonlinear Least-Squares Estimation 116

5.2 Overview of Nonlinear Estimators 117

5.3 Extremum Estimators 124

5.4 Estimating Equations 133

5.5 Statistical Inference 135

5.6 Maximum Likelihood 139

5.7 Quasi-Maximum Likelihood 146

5.8 Nonlinear Least Squares 150

5.9 Example: ML and NLS Estimation 159

6 Generalized Method of Moments and Systems Estimation 166

6.3 Generalized Method of Moments 172

6.4 Linear Instrumental Variables 183

6.5 Nonlinear Instrumental Variables 192

6.6 Sequential Two-Step m-Estimation 200

6.7 Minimum Distance Estimation 202

6.8 Empirical Likelihood 203

6.9 Linear Systems of Equations 206

6.10 Nonlinear Sets of Equations 214

7 Hypothesis Tests 223

7.2 Wald Test 224

7.3 Likelihood-Based Tests 233

7.4 Example: Likelihood-Based Hypothesis Tests 241

7.5 Tests in Non-ML Settings 243

7.6 Power and Size of Tests 246

7.7 Monte Carlo Studies 250

7.8 Bootstrap Example 254

8 Specification Tests and Model Selection 259

8.2 m-Tests 260

8.3 Hausman Test 271

8.4 Tests for Some Common Misspecifications 274

8.5 Discriminating between Nonnested Models 278

8.6 Consequences of Testing 285

8.7 Model Diagnostics 287

9 Semiparametric Methods 294

9.2 Nonparametric Example: Hourly Wage 295

9.3 Kernel Density Estimation 298

9.4 Nonparametric Local Regression 307

9.5 Kernel Regression 311

9.6 Alternative Nonparametric Regression Estimators 319

9.7 Semiparametric Regression 322

9.8 Derivations of Mean and Variance of Kernel Estimators 330

10 Numerical Optimization 336

10.3 Specific Methods 341

III Simulation-Based Methods

11 Bootstrap Methods 357

11.2 Bootstrap Summary 358

11.3 Bootstrap Example 366

11.4 Bootstrap Theory 368

11.5 Bootstrap Extensions 373

11.6 Bootstrap Applications 376

12 Simulation-Based Methods 384

12.3 Basics of Computing Integrals 387

12.4 Maximum Simulated Likelihood Estimation 393

12.5 Moment-Based Simulation Estimation 398

12.6 Indirect Inference 404

12.7 Simulators 406

12.8 Methods of Drawing Random Variates 410

13 Bayesian Methods 419

13.2 Bayesian Approach 420

13.3 Bayesian Analysis of Linear Regression 435

13.4 Monte Carlo Integration 443

13.5 Markov Chain Monte Carlo Simulation 445

13.6 MCMC Example: Gibbs Sampler for SUR 452

13.7 Data Augmentation 454

13.8 Bayesian Model Selection 456

IV Models for Cross-Section Data

14 Binary Outcome Models 463

14.2 Binary Outcome Example: Fishing Mode Choice 464

14.3 Logit and Probit Models 465

14.4 Latent Variable Models 475

14.5 Choice-Based Samples 478

14.6 Grouped and Aggregate Data 480

14.7 Semiparametric Estimation 482

14.8 Derivation of Logit from Type I Extreme Value 486

15 Multinomial Models 490

15.2 Example: Choice of Fishing Mode 491

15.3 General Results 495

15.4 Multinomial Logit 500

15.5 Additive Random Utility Models 504

15.6 Nested Logit 507

15.7 Random Parameters Logit 512

15.8 Multinomial Probit 516

15.9 Ordered, Sequential, and Ranked Outcomes 519

15.10 Multivariate Discrete Outcomes 521

15.11 Semiparametric Estimation 523

15.12 Derivations for MNL, CL, and NL Models 524

16 Tobit and Selection Models 529

16.2 Censored and Truncated Models 530

16.3 Tobit Model 536

16.4 Two-Part Model 544

16.5 Sample Selection Models 546

16.6 Selection Example: Health Expenditures 553

16.7 Roy Model 555

16.8 Structural Models 558

16.9 Semiparametric Estimation 562

16.10 Derivations for the Tobit Model 566

17 Transition Data: Survival Analysis 573

17.2 Example: Duration of Strikes 574

17.4 Censoring 579

17.5 Nonparametric Models 580

17.6 Parametric Regression Models 584

17.7 Some Important Duration Models 591

17.8 Cox PH Model 592

17.9 Time-Varying Regressors 597

17.10 Discrete-Time Proportional Hazards 600

17.11 Duration Example: Unemployment Duration 603

18 Mixture Models and Unobserved Heterogeneity 611

18.2 Unobserved Heterogeneity and Dispersion 612

18.3 Identification in Mixture Models 618

18.4 Specification of the Heterogeneity Distribution 620

18.5 Discrete Heterogeneity and Latent Class Analysis 621

18.6 Stock and Flow Sampling 625

18.7 Specification Testing 628

18.8 Unobserved Heterogeneity Example: Unemployment Duration 632

19 Models of Multiple Hazards 640

19.2 Competing Risks 642

19.3 Joint Duration Distributions 648

19.4 Multiple Spells 655

19.5 Competing Risks Example: Unemployment Duration 658

20 Models of Count Data 665

20.2 Basic Count Data Regression 666

20.3 Count Example: Contacts with Medical Doctor 671

20.4 Parametric Count Regression Models 674

20.5 Partially Parametric Models 682

20.6 Multivariate Counts and Endogenous Regressors 685

20.7 Count Example: Further Analysis 690

V Models for Panel Data

21 Linear Panel Models: Basics 697

21.2 Overview of Models and Estimators 698

21.3 Linear Panel Example: Hours and Wages 708

21.4 Fixed Effects versus Random Effects Models 715

21.5 Pooled Models 720

21.6 Fixed Effects Model 726

21.7 Random Effects Model 734

21.8 Modeling Issues 737

22 Linear Panel Models: Extensions 743

22.2 GMM Estimation of Linear Panel Models 744

22.3 Panel GMM Example: Hours and Wages 754

22.4 Random and Fixed Effects Panel GMM 756

22.5 Dynamic Models 763

22.6 Difference-in-Differences Estimator 768

22.7 Repeated Cross Sections and Pseudo Panels 770

22.8 Mixed Linear Models 774

23 Nonlinear Panel Models 779

23.2 General Results 779

23.3 Nonlinear Panel Example: Patents and R&D 762

23.4 Binary Outcome Data 795

23.5 Tobit and Selection Models 800

23.6 Transition Data 801

23.7 Count Data 802

23.8 Semiparametric Estimation 808

VI Further Topics

24 Stratified and Clustered Samples 813

24.2 Survey Sampling 814

24.3 Weighting 817

24.4 Endogenous Stratification 822

24.5 Clustering 829

24.6 Hierarchical Linear Models 845

24.7 Clustering Example: Vietnam Health Care Use 848

24.8 Complex Surveys 853

25 Treatment Evaluation 860

25.2 Setup and Assumptions 862

25.3 Treatment Effects and Selection Bias 865

25.4 Matching and Propensity Score Estimators 871

25.5 Differences-in-Differences Estimators 878

25.6 Regression Discontinuity Design 879

25.7 Instrumental Variable Methods 883

25.8 Example: The Effect of Training on Earnings 889

26 Measurement Error Models 899

26.2 Measurement Error in Linear Regression 900

26.3 Identification Strategies 905

26.4 Measurement Errors in Nonlinear Models 911

26.5 Attenuation Bias Simulation Examples 919

27 Missing Data and Imputation 923

27.2 Missing Data Assumptions 925

27.3 Handling Missing Data without Models 928

27.4 Observed-Data Likelihood 929

27.5 Regression-Based Imputation 930

27.6 Data Augmentation and MCMC 932

27.7 Multiple Imputation 934

27.8 Missing Data MCMC Imputation Example 935

A Asymptotic Theory 943

A.2 Convergence in Probability 944

A.3 Laws of Large Numbers 947

A.4 Convergence in Distribution 948

A.5 Central Limit Theorems 949

A.6 Multivariate Normal Limit Distributions 951

A.7 Stochastic Order of Magnitude 954

A.8 Other Results 955

B Making Pseudo-Random Draws 957.

Notes:

Includes bibliographical references (pages 961-997) and indexes.

Local Notes:

Acquired for the Penn Libraries with assistance from the Charles R. Anderson Endowment Fund.

ISBN:

9780521848053

0521848059

OCLC:

56599620

Online:

Publisher description

Contributor biographical information