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Analysis of Panel Data, Second Edition

Cheng Hsiao
Publisher: Cambridge University Press
Copyright: 2003
ISBN-13: 978-0-521-52271-7
Pages: 366; paperback
Price: $35.75

Comment from the Stata technical group

The second edition of Hsiao’s Analysis of Panel Data is a complete revision of the 1986 version that has served as an essential reference on panel-data models. With the increased availability of panel datasets, this book will be welcomed not only by economists but also by researchers in other social sciences, business, and the natural sciences. The judicious combination of theory and applications makes Hsiao’s monograph useful both as a textbook for students and as a reference manual for practitioners.

The first three chapters of the book provide a detailed introduction to static random- and fixed-effects models, including model estimation, specification testing, and treatment of heteroskedasticity and correlation. Chapter 4 is a concise overview of dynamic panel-data models, including the GMM estimators that have become quite popular in recent years, and Hsiao does a superb job of bringing the literature together into one cohesive discussion. Simultaneous-equation estimation is covered in chapter 5.

Chapter 6 is devoted to variable-coefficient models, including Swamy’s random coefficients model and its relatives: fixed-coefficients models and their dynamic counterparts. Chapters 7 and 8 discuss parametric and semiparametric approaches to limited dependent-variable models and provide many references to the growing literature in this field; chapter 7 also includes a clear presentation of the incidental parameters problem. Chapters 9 and 10 present a smorgasbord of further topics, including incomplete panels, pseudopanels, large-N and large-T asymptotics, and multilevel models. The sections in these two chapters are rather brief, but they do provide many references to the relevant literature. Chapter 11 summarizes the use of panel-data models.

In short, the second edition of Analysis of Panel Data will prove to be an invaluable reference to users of panel data, just as the first edition has for 14 years.

Table of contents

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 Bt 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
Author Index
Subject Index
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