Preface to the Second Edition

Preface to the First Edition

1. Introduction

1.1 Advantages of Panel Data

1.2 Issues Involved in Utilizing Panel Data

1.2.1 Heterogeneity Bias

1.2.2 Selectivity Bias

1.3 Outline of the Monograph

2. 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 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

3.4.2.a Mundlak’s Formulation

3.4.2.b Conditional and Unconditional Inferences in the
Presence or Absence of Correlation between Individual
Effects and Attributes

3.5 Tests for Misspecification

3.6 Models with Specific 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

3.8 Models with Serially Correlated Errors

3.9 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 Covariance Estimator

4.3 The Random-Effects Models

4.3.1 Bias in the OLS Estimator

4.3.2 Model Formulation

4.3.3 Estimation of Random-Effects Models

4.3.3.a Maximum Likelihood Estimator

4.3.3.b Generalized-Least-Squares Estimator

4.3.3.c Instrumental-Variable Estimator

4.3.3.d Generalized Methods of Moments Estimator

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
Generalized Method of Moments Estimator (GMM)

4.5.4 Random- versus Fixed-Effects Specification

4.6 Estimation of Dynamic Models with Arbitrary Correlations in the
Residuals

4.7 Fixed-Effects Vector Autoregressive Models

4.7.1 Model Formulation

4.7.2 Generalized Method of Moments (GMM) Estimation

4.7.3 (Transformed) Maximum Likelihood Estimator

4.7.4 Minimum-Distance Estimator

Appendix 4A: Derivation of the Asymptotic Covariance Matrix of the
Feasible MDE

5. 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

5.4.2.a Instrumental-Variable Method

5.4.2.b Maximum-Likelihood Method

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

6.2.2.a The Model

6.2.2.b Estimation

6.2.2.c Predicting Individual Coefficients

6.2.2.d Testing for Coefficient Variation

6.2.2.e Fixed or Random Coefficients

6.2.2.f An Example

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 B_{t} 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 An Example

6.6.4 Random or Fixed Parameters

6.6.4.a An Example

6.6.4.b Model Selection

6.7 Dynamic Random-Coefficient Models

6.8 An Example — Liquidity Constraints and Firm Investment Expenditure

Appendix 6A: Combination of Two Normal Distributions

7. Discrete Data

7.1 Introduction

7.2 Some Discrete-Response Models

7.3 Parametric Approach to Static Models with Heterogeneity

7.3.1 Fixed-Effects Models

7.3.1.a Maximum Likelihood Estimator

7.3.1.b Conditions for the Existence of a Consistent Estimator

7.3.1.c Some Monte Carlo Evidence

7.3.2 Random-Effects Model

7.4 Semiparametric Approach to Static Models

7.4.1 Maximum Score Estimator

7.4.2 A Root-*N* 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

7.5.5.a Female Employment

7.5.5.b Household Brand Choices

8. Truncated and Censored Data

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

8.4.1.a Truncated Regression

8.4.1.b 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. Incomplete Panel Data

9.1 Estimating Distributed Lags in Short Panels

9.1.1 Introduction
9.1.2 Common Assumptions

9.1.3 Identification Using Prior Structure of the Process of the
Exogenous Variable

9.1.4 Identification Using Prior Structure of the Lag Coefficients
9.1.5 Estimation and Testing

9.2 Rotating or Randomly Missing Data

9.3 Pseudopanels (or Repeated Cross-Sectional Data)

9.4 Pooling of a Single Cross-Sectional and a Single Time-Series
Data Set

9.4.1 Introduction

9.4.2 The Likelihood Approach to Pooling Cross-Sectional and
Time-Series Data

9.4.3 An Example

10. Miscellaneous Topics

10.1 Simulation Methods

10.2 Panels with Large *N* and *T*

10.3 Unit-Root Tests

10.4 Data with Multilevel Structures

10.5 Errors of Measurement

10.6 Modeling Cross-Sectional Dependence

11. A Summary View

11.1 Introduction

11.2 Benefits and Limitations of Panel Data

11.2.1 Increasing Degrees of Freedom and Lessening the Problem
of Multicollinearity

11.2.2 Identification and Discrimination between Competing
Hypotheses

11.2.3 Reducing Estimation Bias

11.2.3.a Omitted-Variable Bias

11.2.3.b Bias Induced by the Dynamic Structure of a Model

11.2.3.c Simultaneity Bias

11.2.3.d Bias Induced by Measurement Errors

11.2.4 Providing Micro Foundations for Aggregate Data Analysis

11.3 Efficiency of the Estimates

Notes

References

Author Index

Subject Index