Econometric analysis of cross section and panel data

This text provides a rigorous treatment of contemporary methods used in microeconometric research. The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association.

Preface	p. xxi
Acknowledgements	p. xxix
I Introduction and Background
1 Introduction
1.1 Causal Relationships and Ceteris Paribus Analysis	p. 3
1.2 Stochastic Setting and Asymptotic Analysis	p. 4
1.3 Some Examples	p. 7
1.4 Why Not Fixed Explanatory Variables?	p. 9
2 Conditional Expectations and Related Concepts in Econometrics
2.1 Role of Conditional Expectations in Econometrics	p. 13
2.2 Features of Conditional Expectations	p. 14
2.3 Linear Projections	p. 25
Problems	p. 27
Appendix 2A	p. 30
3 Basic Asymptotic Theory
3.1 Convergence of Deterministic Sequences	p. 37
3.2 Convergence in Probability and Boundedness in Probability	p. 38
3.3 Convergence in Distribution	p. 40
3.4 Limit Theorems for Random Samples	p. 41
3.5 Limiting Behavior of Estimators and Test Statistics	p. 42
Problems	p. 47
II Linear Models
4 Single-Equation Linear Model and Ordinary Least Squares Estimation
4.1 Overview of the Single-Equation Linear Model	p. 53
4.2 Asymptotic Properties of Ordinary Least Squares	p. 55
4.3 Ordinary Least Squares Solutions to the Omitted Variables Problem	p. 65
4.4 Properties of Ordinary Least Squares under Measurement Error	p. 76
5 Instrumental Variables Estimation of Single-Equation Linear Models
5.1 Instrumental Variables and Two-Stage Least Squares	p. 89
5.2 General Treatment of Two-Stage Least Squares	p. 98
5.3 IV Solutions to the Omitted Variables and Measurement Error Problems	p. 112
Problems	p. 115
6 Additional Single-Equation Topics
6.1 Estimation with Generated Regressors and Instruments	p. 123
6.2 Control Function Approach to Endogeneity	p. 126
6.3 Some Specification Tests	p. 129
6.4 Correlated Random Coe‰cient Models	p. 141
6.5 Pooled Cross Sections and Di¤erence-in-Di¤erences Estimation	p. 146
Problems	p. 152
Appendix 6A	p. 157
7 Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares
7.1 Introduction	p. 161
7.2 Some Examples	p. 161
7.3 System Ordinary Least Squares Estimation of a Multivariate Linear System	p. 166
7.4 Consistency and Asymptotic Normality of Generalized Least Squares	p. 173
7.5 Feasible Generalized Least Squares	p. 176
7.6 Testing the Use of Feasible Generalized Least Squares	p. 183
7.7 Seemingly Unrelated Regressions, Revisited	p. 185
7.8 Linear Panel Data Model, Revisited	p. 191
Problems	p. 202
8 System Estimation by Instrumental Variables
8.1 Introduction and Examples	p. 207
8.2 General Linear System of Equations	p. 210
8.3 Generalized Method of Moments Estimation	p. 213
8.4 Generalized Instrumental Variables Estimator	p. 222
8.5 Testing Using Generalized Method of Moments	p. 226
8.6 More E‰cient Estimation and Optimal Instruments	p. 229
8.7 Summary Comments on Choosing an Estimator	p. 232
Problems	p. 233
9 Simultaneous Equations Models
9.1 Scope of Simultaneous Equations Models	p. 239
9.2 Identification in a Linear System	p. 241
9.3 Estimation after Identification	p. 252
9.4 Additional Topics in Linear Simultaneous Equations Methods	p. 256
9.5 Simultaneous Equations Models Nonlinear in Endogenous Variables	p. 262
9.6 Di¤erent Instruments for Di¤erent Equations	p. 271
Problems	p. 273
10 Basic Linear Unobserved E¤ects Panel Data Models
10.1 Motivation: Omitted Variables Problem	p. 281
10.2 Assumptions about the Unobserved E¤ects and Explanatory Variables	p. 285
10.3 Estimating Unobserved E¤ects Models by Pooled Ordinary Least Squares	p. 291
10.4 Random E¤ects Methods	p. 292
10.5 Fixed E¤ects Methods	p. 300
10.6 First Differencing Methods	p. 315
10.7 Comparison of Estimators	p. 321
Problems	p. 334
11 More Topics in Linear Unobserved Effects Models
11.1 Generalized Method of Moments Approaches to the Standard Linear Unobserved E¤ects Model	p. 345
11.2 Random and Fixed E¤ects Instrumental Variables Methods	p. 349
11.3 Hausman and Taylor-Type Models	p. 358
11.4 First Di¤erencing Instrumental Variables Methods	p. 361
11.5 Unobserved Effects Models with Measurement Error	p. 365
11.6 Estimation under Sequential Exogeneity	p. 368
11.7 Models with Individual-Specific Slopes	p. 374
Problems	p. 387
III General Approaches to Nonlinear Estimation
12 M-Estimation, Nonlinear Regression, and Quantile Regression
12.1 Introduction	p. 397
12.2 Identification, Uniform Convergence, and Consistency	p. 401
12.3 Asymptotic Normality	p. 405
12.4 Two-Step M-Estimators	p. 409
12.5 Estimating the Asymptotic Variance	p. 413
12.6 Hypothesis Testing	p. 420
12.7 Optimization Methods	p. 431
12.8 Simulation and Resampling Methods	p. 436
12.9 Multivariate Nonlinear Regression Methods	p. 442
12.10 Quantile Estimation	p. 449
Problems	p. 462
13 Maximum Likelihood Methods
13.1 Introduction	p. 469
13.2 Preliminaries and Examples	p. 470
13.3 General Framework for Conditional Maximum Likelihood Estimation	p. 473
13.4 Consistency of Conditional Maximum Likelihood Estimation	p. 475
13.5 Asymptotic Normality and Asymptotic Variance Estimation	p. 476
13.6 Hypothesis Testing	p. 481
13.7 Specification Testing	p. 482
13.8 Partial (or Pooled) Likelihood Methods for Panel Data	p. 485
13.9 Panel Data Models with Unobserved E¤ects	p. 494
13.10 Two-Step Estimators Involving Maximum Likelihood	p. 499
13.11 Quasi-Maximum Likelihood Estimation	p. 502
Problems	p. 517
Appendix 13A	p. 522
14 Generalized Method of Moments and Minimum Distance Estimation
14.1 Asymptotic Properties of Generalized Method of Moments	p. 525
14.2 Estimation under Orthogonality Conditions	p. 530
14.3 Systems of Nonlinear Equations	p. 532
14.4 Efficient Estimation	p. 538
14.5 Classical Minimum Distance Estimation	p. 545
14.6 Panel Data Applications	p. 547
Problems	p. 555
Appendix 14A	p. 558
IV Nonlinear Models and Related Topics
15 Binary Response Models
15.1 Introduction	p. 561
15.2 Linear Probability Model for Binary Response	p. 562
15.3 Index Models for Binary Response: Probit and Logit	p. 565
15.4 Maximum Likelihood Estimation of Binary Response Index Models	p. 567
15.5 Testing in Binary Response Index Models	p. 569
15.6 Reporting the Results for Probit and Logit	p. 573
15.7 Specification Issues in Binary Response Models	p. 582
15.8 Binary Response Models for Panel Data	p. 608
Problems	p. 635
16 Multinomial and Ordered Response Models
16.1 Introduction	p. 643
16.2 Multinomial Response Models	p. 643
16.3 Ordered Response Models	p. 655
Problems	p. 663
17 Corner Solution Responses
17.1 Motivation and Examples	p. 667
17.2 Useful Expressions for Type I Tobit	p. 671
17.3 Estimation and Inference with the Type I Tobit Model	p. 676
17.4 Reporting the Results	p. 677
17.5 Specification Issues in Tobit Models	p. 680
17.6 Two-Part Models and Type II Tobit for Corner Solutions	p. 690
17.7 Two-Limit Tobit Model	p. 703
17.8 Panel Data Methods	p. 705
Problems	p. 715
18 Count, Fractional, and Other Nonnegative Responses
18.1 Introduction	p. 723
18.2 Poisson Regression	p. 724
18.3 Other Count Data Regression Models	p. 736
18.4 Gamma (Exponential) Regression Model	p. 740
18.5 Endogeneity with an Exponential Regression Function	p. 742
18.6 Fractional Responses	p. 748
18.7 Panel Data Methods	p. 755
Problems	p. 769
19 Censored Data, Sample Selection, and Attrition
19.1 Introduction	p. 777
19.2 Data Censoring	p. 778
19.3 Overview of Sample Selection	p. 790
19.4 When Can Sample Selection Be Ignored?	p. 792
19.5 Selection on the Basis of the Response Variable: Truncated Regression	p. 799
19.6 Incidental Truncation: A Probit Selection Equation	p. 802
19.7 Incidental Truncation: A Tobit Selection Equation	p. 815
19.8 Inverse Probability Weighting for Missing Data	p. 821
19.9 Sample Selection and Attrition in Linear Panel Data Models	p. 827
Problems	p. 845
20 Stratified Sampling and Cluster Sampling
20.1 Introduction	p. 854
20.2 Stratified Sampling	p. 854
20.3 Cluster Sampling	p. 863
20.4 Complex Survey Sampling	p. 894
Problems	p. 899
21 Estimating Average Treatment Effects
21.1 Introduction	p. 903
21.2 A Counterfactual Setting and the Self-Selection Problem	p. 904
21.36 Methods Assuming Ignorability (or Unconfoundedness) of Treatment	p. 908
21.4 Instrumental Variables Methods	p. 937
21.5 Regression Discontinuity Designs	p. 954
21.6 Further Issues	p. 960
Problems	p. 975
22 Duration Analysis
22.1 Introduction	p. 983
22.2 Hazard Functions	p. 984
22.3 Analysis of Single-Spell Data with Time-Invariant Covariates	p. 991
22.4 Analysis of Grouped Duration Data	p. 1010
22.5 Further Issues	p. 1018
Problems	p. 1019
References	p. 1025
Index	p. 1045

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