Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
1. Introduction
1.1 Introduction
1.2 Advantages of Panel Data
1.3 Issues Involved in Utilizing Panel Data
1.3.1 Unobserved Heterogeneity across Individuals and over Time
1.3.2 Incidental Parameters and Multidimensional Statistics
1.3.3 Sample Attrition
1.4 Outline of the Monograph
2. Homogeneity Tests for Linear Regression Models (Analysis
of Covariance)
2.1 Introduction
2.2 Analysis of Covariance
2.3 An Example
3. Simple Regression with Variable Intercepts
3.1 Introduction
3.2 Fixed-Effects Models: Least-Squares Dummy Variable Approach
3.3 Random-Effects Models: Estimation of Variance-Components Models
3.3.1 Covariance Estimation
3.3.2 Generalized Least-Squares (GLS) Estimation
3.3.3 Maximum-Likelihood Estimation
3.4 Fixed Effects or Random Effects
3.4.1 An Example
3.4.2 Conditional Inference or Unconditional (Marginal) Inference
3.5 Tests for Misspecification
3.6 Models with Time- and/or Individual-Invariant Explanatory Variables and Both Individual- and Time-Specific Effects
3.6.1 Estimation of Models with Individual-Specific Variables
3.6.2 Estimation of Models with Both Individual and Time Effects
3.7 Heteroscedasticity and Autocorrelation
3.7.1 Heteroscedasticity
3.7.2 Models with Serially Correlated Errors
3.7.3 Heteroscedasticity Autocorrelation Consistent Estimator for
the Covariance Matrix of the CV Estimator
3.8 Models with Arbitrary Error Structure – Chamberlain π-
Approach
Appendix 3A: Consistency and Asymptotic Normality of the
Minimum-Distance Estimator
Appendix 3B: Characteristic Vectors and the Inverse of the
Variance–Covariance Matrix of a Three-Component Model
4. Dynamic Models with Variable Intercepts
4.1 Introduction
4.2 The CV Estimator
4.3 Random-Effects Models
4.3.1 Bias in the OLS Estimator
4.3.2 Model Formulation
4.3.3 Estimation of Random-Effects Models
4.3.4 Testing Some Maintained Hypotheses on Initial Conditions
4.3.5 Simulation Evidence
4.4 An Example
4.5 Fixed-Effects Models
4.5.1 Transformed Likelihood Approach
4.5.2 Minimum-Distance Estimator
4.5.3 Relations between the Likelihood-Based Estimator and the GMM
4.5.4 Issues of Random versus Fixed-Effects Specification
4.6 Estimation of Dynamic Models with Arbitrary Serial Correlations in
the Residuals
4.7 Models with Both Individual- and Time-Specific Additive Effects
Appendix 4A: Derivation of the Asymptotic Covariance Matrix
of Feasible MDE
Appendix 4B: Large
Ν and
Τ Asymptotics
5. Static Simultaneous-Equations Models
5.1 Introduction
5.2 Joint Generalized Least-Squares Estimation Technique
5.3 Estimation of Structural Equations
5.3.1 Estimation of a Single Equation in the Structural Model
5.3.2 Estimation of the Complete Structural System
5.4 Triangular System
5.4.1 Identification
5.4.2 Estimation
5.4.3 An Example
Appendix 5A
6. Variable-Coefficient Models
6.1 Introduction
6.2 Coefficients that Vary over Cross-Sectional Units
6.2.1 Fixed-Coefficient Model
6.2.2 Random-Coefficient Model
6.3 Coefficients that Vary over Time and Cross-Sectional Units
6.3.1 The Model
6.3.2 Fixed-Coefficient Model
6.3.3 Random-Coefficient Model
6.4 Coefficients that Evolve over Time
6.4.1 The Model
6.4.2 Predicting βt by the Kalman Filter
6.4.3 Maximum-Likelihood Estimation
6.4.4 Tests for Parameter Constancy
6.5 Coefficients that Are Functions of Other Exogenous Variables
6.6 A Mixed Fixed- and Random-Coefficients Model
6.6.1 Model Formulation
6.6.2 A Bayes Solution
6.6.3 Random or Fixed Differences?
6.7 Dynamic Random-Coefficients Models
6.8 Two Examples
6.8.1 Liquidity Constraints and Firm Investment Expenditure
6.8.2 Aggregate versus Disaggregate Analysis
6.9 Correlated Random-Coefficients Models
6.9.1 Introduction
6.9.2 Identification with Cross-Sectional Data
6.9.3 Estimation of the Mean Effects with Panel Data
Appendix 6A: Combination of Two Normal Distributions
7. Discrete Data
7.1 Introduction
7.2 Some Discrete-Response Models for Cross-Sectional Data
7.3 Parametric Approach to Static Models with Heterogeneity
7.3.1 Fixed-Effects Models
7.3.2 Random-Effects Models
7.4 Semiparametric Approach to Static Models
7.4.1 Maximum Score Estimator
7.4.2 A Root-Ν Consistent Semiparametric Estimator
7.5 Dynamic Models
7.5.1 The General Model
7.5.2 Initial Conditions
7.5.3 A Conditional Approach
7.5.4 State Dependence versus Heterogeneity
7.5.5 Two Examples
7.6 Alternative Approaches for Identifying State Dependence
7.6.1 Bias-Adjusted Estimator
7.6.2 Bounding Parameters
7.6.3 Approximate Model
8. Sample Truncation and Sample Selection
8.1 Introduction
8.2 An Example – Nonrandomly Missing Data
8.2.1 Introduction
8.2.2 A Probability Model of Attrition and Selection Bias
8.2.3 Attrition in the Gary Income-Maintenance Experiment
8.3 Tobit Models with Random Individual Effects
8.4 Fixed-Effects Estimator
8.4.1 Pairwise Trimmed Least-Squares and Least Absolute
Deviation Estimators for Truncated and Censored
Regressions
8.4.2 A Semiparametric Two-Step Estimator for the Endogenously
Determined Sample Selection Model
8.5 An Example: Housing Expenditure
8.6 Dynamic Tobit Models
8.6.1 Dynamic Censored Models
8.6.2 Dynamic Sample Selection Models
9. Cross-Sectionally Dependent Panel Data
9.1 Issues of Cross-Sectional Dependence
9.2 Spatial Approach
9.2.1 Introduction
9.2.2 Spatial Error Model
9.2.3 Spatial Lag Model
9.2.4 Spatial Error Models with Individual-Specific Effects
9.2.5 Spatial Lag Model with Individual-Specific Effects
9.2.6 Spatial Dynamic Panel Data Models
9.3 Factor Approach
9.4 Group Mean Augmented (Common Correlated Effects) Approach to
Control the Impact of Cross-Sectional Dependence
9.5 Test of Cross-Sectional Independence
9.5.1 Linear Model
9.5.2 Limited Dependent-Variable Model
9.5.3 An Example – A Housing Price Model of China
9.6 A Panel Data Approach for Program Evaluation
9.6.1 Introduction
9.6.2 Definition of Treatment Effects
9.6.3 Cross-Sectional Adjustment Methods
9.6.4 Panel Data Approach
10. Dynamic System
10.1 Panel Vector Autoregressive Models
10.1.1 "Homogeneous" Panel VAR Models
10.1.2 Heterogeneous Vector Autoregressive Models
10.2 Cointegrated Panel Models and Vector Error Correction
10.2.1 Properties of Cointegrated Processes
10.2.2 Estimation
10.3 Unit Root and Cointegration Tests
10.3.1 Unit Root Tests
10.3.2 Tests of Cointegration
10.4 Dynamic Simultaneous Equations Models
10.4.1 The Model
10.4.2 Likelihood Approach
10.4.3 Method of Moments Estimator
11. Incomplete Panel Data
11.1 Rotating or Randomly Missing Data
11.2 Pseudo-Panels (or Repeated Cross-Sectional Data)
11.3 Pooling of Single Cross-Sectional and Single Time Series Data
11.3.1 Introduction
11.3.2 The Likelihood Approach to Pooling Cross-Sectional and
Time Series Data
11.3.3 An Example
11.4 Estimating Distributed Lags in Short Panels
11.4.1 Introduction
11.4.2 Common Assumptions
11.4.3 Identification Using Prior Structure on the Process of
the Exogenous Variable
11.4.4 Identification Using Prior Structure on the Lag
Coefficients
11.4.5 Estimation and Testing
12. Miscellaneous Topics
12.1 Duration Model
12.2 Count Data Model
12.3 Panel Quantile Regression
12.4 Simulation Methods
12.5 Data with Multilevel Structures
12.6 Errors of Measurement
12.7 Nonparametric Panel Data Models
13. A Summary View
13.1 Benefits of Panel Data
13.1.1 Increasing Degrees of Freedom and Lessening the Problem
of Multicollinearity
13.1.2 Identification and Discrimination between Competing
Hypotheses
13.1.3 Reducing Estimation Bias
13.1.4 Generating More Accurate Predictions for Individual
Outcomes
13.1.5 Providing Information on Appropriate Level of
Aggregation
13.1.6 Simplifying Computation and Statistical Inference
13.2 Challenges for Panel Data Analysis
13.2.1 Modeling Unobserved Heterogeneity
13.2.2 Controlling the Impact of Unobserved Heterogeneity in
Nonlinear Models
13.2.3 Modeling Cross-Sectional Dependence
13.2.4 Multidimensional Asymptotics
13.2.5 Sample Attrition
13.3 A Concluding Remark
References
Author Index
Subject Index
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