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面板数据经典教材Econometric Analysis of Cross Section and Panel Data
作者Je¤rey M. Wooldridge目录太多，值得一看，文字比较简单
Contents
Preface xvii
Acknowledgments xxiii
I INTRODUCTION AND BACKGROUND 1
1 Introduction 3
1.1 Causal Relationships and Ceteris Paribus Analysis 3
1.2 The Stochastic Setting and Asymptotic Analysis 4
1.2.1 Data Structures 4
1.2.2 Asymptotic Analysis 7
1.3 Some Examples 7
1.4 Why Not Fixed Explanatory Variables? 9
2 Conditional Expectations and Related Concepts in Econometrics 13
2.1 The Role of Conditional Expectations in Econometrics 13
2.2 Features of Conditional Expectations 14
2.2.1 Definition and Examples 14
2.2.2 Partial E¤ects, Elasticities, and Semielasticities 15
2.2.3 The Error Form of Models of Conditional Expectations 18
2.2.4 Some Properties of Conditional Expectations 19
2.2.5 Average Partial E¤ects 22
2.3 Linear Projections 24
Problems 27
Appendix 2A 29
2.A.1 Properties of Conditional Expectations 29
2.A.2 Properties of Conditional Variances 31
2.A.3 Properties of Linear Projections 32
3 Basic Asymptotic Theory 35
3.1 Convergence of Deterministic Sequences 35
3.2 Convergence in Probability and Bounded in Probability 36
3.3 Convergence in Distribution 38
3.4 Limit Theorems for Random Samples 39
3.5 Limiting Behavior of Estimators and Test Statistics 40
3.5.1 Asymptotic Properties of Estimators 40
3.5.2 Asymptotic Properties of Test Statistics 43
Problems
II LINEAR MODELS 47
4 The Single-Equation Linear Model and OLS Estimation 49
4.1 Overview of the Single-Equation Linear Model 49
4.2 Asymptotic Properties of OLS 51
4.2.1 Consistency 52
4.2.2 Asymptotic Inference Using OLS 54
4.2.3 Heteroskedasticity-Robust Inference 55
4.2.4 Lagrange Multiplier (Score) Tests 58
4.3 OLS Solutions to the Omitted Variables Problem 61
4.3.1 OLS Ignoring the Omitted Variables 61
4.3.2 The Proxy Variable–OLS Solution 63
4.3.3 Models with Interactions in Unobservables 67
4.4 Properties of OLS under Measurement Error 70
4.4.1 Measurement Error in the Dependent Variable 71
4.4.2 Measurement Error in an Explanatory Variable 73
Problems 76
5 Instrumental Variables Estimation of Single-Equation Linear Models 83
5.1 Instrumental Variables and Two-Stage Least Squares 83
5.1.1 Motivation for Instrumental Variables Estimation 83
5.1.2 Multiple Instruments: Two-Stage Least Squares 90
5.2 General Treatment of 2SLS 92
5.2.1 Consistency 92
5.2.2 Asymptotic Normality of 2SLS 94
5.2.3 Asymptotic E‰ciency of 2SLS 96
5.2.4 Hypothesis Testing with 2SLS 97
5.2.5 Heteroskedasticity-Robust Inference for 2SLS 100
5.2.6 Potential Pitfalls with 2SLS 101
5.3 IV Solutions to the Omitted Variables and Measurement Error
Problems 105
5.3.1 Leaving the Omitted Factors in the Error Term 105
5.3.2 Solutions Using Indicators of the Unobservables 105
Problems 107
6 Additional Single-Equation Topics 115
6.1 Estimation with Generated Regressors and Instruments 115
6.1.1 OLS with Generated Regressors 115
6.1.2 2SLS with Generated Instruments 116
6.1.3 Generated Instruments and Regressors 117
6.2 Some Specification Tests 118
6.2.1 Testing for Endogeneity 118
6.2.2 Testing Overidentifying Restrictions 122
6.2.3 Testing Functional Form 124
6.2.4 Testing for Heteroskedasticity 125
6.3 Single-Equation Methods under Other Sampling Schemes 128
6.3.1 Pooled Cross Sections over Time 128
6.3.2 Geographically Stratified Samples 132
6.3.3 Spatial Dependence 134
6.3.4 Cluster Samples 134
Problems 135
Appendix 6A 139
7 Estimating Systems of Equations by OLS and GLS 143
7.1 Introduction 143
7.2 Some Examples 143
7.3 System OLS Estimation of a Multivariate Linear System 147
7.3.1 Preliminaries 147
7.3.2 Asymptotic Properties of System OLS 148
7.3.3 Testing Multiple Hypotheses 153
7.4 Consistency and Asymptotic Normality of Generalized Least
Squares 153
7.4.1 Consistency 153
7.4.2 Asymptotic Normality 156
7.5 Feasible GLS 157
7.5.1 Asymptotic Properties 157
7.5.2 Asymptotic Variance of FGLS under a Standard
Assumption 160
7.6 Testing Using FGLS 162
7.7 Seemingly Unrelated Regressions, Revisited 163
7.7.1 Comparison between OLS and FGLS for SUR Systems 164
7.7.2 Systems with Cross Equation Restrictions 167
7.7.3 Singular Variance Matrices in SUR Systems
7.8 The Linear Panel Data Model, Revisited 169
7.8.1 Assumptions for Pooled OLS 170
7.8.2 Dynamic Completeness 173
7.8.3 A Note on Time Series Persistence 175
7.8.4 Robust Asymptotic Variance Matrix 175
7.8.5 Testing for Serial Correlation and Heteroskedasticity after
Pooled OLS 176
7.8.6 Feasible GLS Estimation under Strict Exogeneity 178
Problems 179
8 System Estimation by Instrumental Variables 183
8.1 Introduction and Examples 183
8.2 A General Linear System of Equations 186
8.3 Generalized Method of Moments Estimation 188
8.3.1 A General Weighting Matrix 188
8.3.2 The System 2SLS Estimator 191
8.3.3 The Optimal Weighting Matrix 192
8.3.4 The Three-Stage Least Squares Estimator 194
8.3.5 Comparison between GMM 3SLS and Traditional 3SLS 196
8.4 Some Considerations When Choosing an Estimator 198
8.5 Testing Using GMM 199
8.5.1 Testing Classical Hypotheses 199
8.5.2 Testing Overidentification Restrictions 201
8.6 More E‰cient Estimation and Optimal Instruments 202
Problems 205
9 Simultaneous Equations Models 209
9.1 The Scope of Simultaneous Equations Models 209
9.2 Identification in a Linear System 211
9.2.1 Exclusion Restrictions and Reduced Forms 211
9.2.2 General Linear Restrictions and Structural Equations 215
9.2.3 Unidentified, Just Identified, and Overidentified Equations 220
9.3 Estimation after Identification 221
9.3.1 The Robustness-E‰ciency Trade-o¤ 221
9.3.2 When Are 2SLS and 3SLS Equivalent? 224
9.3.3 Estimating the Reduced Form Parameters 224
9.4 Additional Topics in Linear SEMs 2259.4.1 Using Cross Equation Restrictions to Achieve Identification 225
9.4.2 Using Covariance Restrictions to Achieve Identification 227
9.4.3 Subtleties Concerning Identification and E‰ciency in Linear
Systems 229
9.5 SEMs Nonlinear in Endogenous Variables 230
9.5.1 Identification 230
9.5.2 Estimation 235
9.6 Di¤erent Instruments for Di¤erent Equations 237
Problems 239
10 Basic Linear Unobserved E¤ects Panel Data Models 247
10.1 Motivation: The Omitted Variables Problem 247
10.2 Assumptions about the Unobserved E¤ects and Explanatory
Variables 251
10.2.1 Random or Fixed E¤ects? 251
10.2.2 Strict Exogeneity Assumptions on the Explanatory
Variables 252
10.2.3 Some Examples of Unobserved E¤ects Panel Data Models 254
10.3 Estimating Unobserved E¤ects Models by Pooled OLS 256
10.4 Random E¤ects Methods 257
10.4.1 Estimation and Inference under the Basic Random E¤ects
Assumptions 257
10.4.2 Robust Variance Matrix Estimator 262
10.4.3 A General FGLS Analysis 263
10.4.4 Testing for the Presence of an Unobserved E¤ect 264
10.5 Fixed E¤ects Methods 265
10.5.1 Consistency of the Fixed E¤ects Estimator 265
10.5.2 Asymptotic Inference with Fixed E¤ects 269
10.5.3 The Dummy Variable Regression 272
10.5.4 Serial Correlation and the Robust Variance Matrix
Estimator 274
10.5.5 Fixed E¤ects GLS 276
10.5.6 Using Fixed E¤ects Estimation for Policy Analysis 278
10.6 First Di¤erencing Methods 279
10.6.1 Inference 279
10.6.2 Robust Variance Matrix 282
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