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Analysis of Panel Data, Fourth Edition

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Author:	Cheng Hsiao
Publisher:	Cambridge University Press
Copyright:	2022
ISBN-13:	978-1-316-51210-4
Pages:	500; paperback

Author:	Cheng Hsiao
Publisher:	Cambridge University Press
Copyright:	2022
ISBN-13:
Pages:	500; eBook
Price:	$0.00

Author:	Cheng Hsiao
Publisher:	Cambridge University Press
Copyright:	2022
ISBN-13:
Pages:	500; Kindle
Price:	$

Comment from the Stata technical group

Cheng Hsiao's Analysis of Panel Data, Fourth Edition is an essential reference on panel-data models. The fourth edition is a minor but important revision; like the previous three editions, it is a must-have reference book for researchers and graduate students.

The fourth edition retains most of the content from the previous edition, although some chapters were reordered. The first two chapters of the book provide detailed introductions to static random- and fixed-effects models, including model estimation, specification testing, and treatment of heteroskedasticity and correlation.

Chapter 3 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. Estimation of static simultaneous-equation models is covered in chapter 4.

Chapter 5 covers dynamic panel-data systems, including vector autoregressive models, conintegrated models, and time-series-related tests. Chapters 6--8 discuss parametric and semiparametric approaches to limited dependent-variable models and provide many references to the growing literature in this field. Particularly, chapter 6 includes a clear presentation of the incidental parameters problem when it comes to nonlinear panel estimation with individual effects. Hsiao does an excellent job of covering these essential topics.

Chapter 9 presents a smorgasbord of further topics, including simulation, multilevel models, pseudopanels, and missing data. The section is rather brief, but it does provide many references to the relevant literature. Chapter 10 adds new topics, such as mixed effects and factor dimension determination.

Chapter 11 covers cross-sectionally dependent data. Chapter 12 provides tools for evaluating treatment for both cross-sectional and panel data. Chapter 13 is devoted to variable-coefficient models, including Swamy's random-coefficients model and its relatives: fixed-coefficients models and their dynamic counterparts.

Chapter 14 is new to this edition and makes the latest edition more suitable for recent challenges regarding analyzing big and high-dimensional data and using machine learning algorithms.

In short, the fourth edition of Analysis of Panel Data, just like the first three editions, will prove to be an invaluable reference to users of panel data.

View table of contents >>

List of Figures

List of Tables

Preface to the Fourth Edition

1. Introduction

1.1 Introduction
1.2 Advantages of Panel Data
1.3 Challenges to Panel Data Analysis
1.4 Outline of the Monograph

2. Linear Static Models with Additive Effects

2.1 Introduction
2.2 Fixed-Effects Models: Dummy-Variable Approach
2.3 Random-Effects Models: Variance-Components Models
2.4 Fixed Effects or Random Effects
2.5 Tests for Misspecification
2.6 Models with Time-and/or Individual-Invariant Explanatory Variables and both Individual-and Time-Specific Effects
2.7 Analysis of Covariance Tests for the Presence of Individual-or Time-Specific Effects
2.8 Heteroscedasticity and Autocorrelation
2.9 Models with Arbitrary Error Structure: Chamberlain ℼ -Approach

Appendix 2A: Consistency and Asymptotic Normality of the Minimum-Distance Estimator
Appendix 2B: Characteristic Vectors and the Inverse of the Variance–Covariance Matrix of a Three-Component Model

3. Dynamic Models with Additive Specific Effects

3.1 Introduction
3.2 The Least Squares and the Least Squares Dummy Variable (Covariance) Estimator
3.3 Method of Moments Estimator
3.4 The Quasi-Likelihood Approach for Random-Effects Models
3.5 The Likelihood Approach-Fixed-Effects Models
3.6 Relations between the Likelihood Based Estimator and the GMM
3.7 Estimation of Dynamic Models with Arbitrary Serial Correlations in the Residuals
3.8 Models with Both Individual- and Time-Specific Additive Effects
Appendix 3A: Derivation of the Asymptotic Covariance Matrix of the Feasible MDE
Appendix 3B: Large N and T Asymptotics

4. Simultaneous-Equations Models

4.1 Introduction
4.2 Joint Generalized Least Squares Estimation Technique
4.3 Estimation of the Structural Equations
4.4 Triangular System
Appendix 4A: The Determinant and Inverse of the Triangular System Covariance Matrix

5. Dynamic System

5.1 Introduction
5.2 Panel Vector Autoregressive Models
5.3 Cointegrated Panel Models and Vector Error Correction
5.4 Unit Root and Cointegration Tests
5.5 Dynamic Simultaneous Equation Models

6. Discrete Data

6.1 Introduction
6.2 Qualitative Response Models for Cross-Sectional Data
6.3 Panel Parametric Approach to Static Models with Heterogeneity
6.4 Semiparametric Approach to Static Models
6.5 Dynamic Models
6.6 Alternative Approaches for Identifying Dynamic Dependence

7. Limited Dependent and Sample Selection Models

7.1 Cross-Sectional Data Approach
7.2 A Sample Selection Example: Nonrandomly Missing Data in a Panel
7.3 Tobit Models with Random Individual Effects
7.4 Fixed-Effects Estimator
7.5 An Example: Housing Expenditure
7.6 Dynamic Tobit Models

8. Some Nonlinear Models

8.1 Duration Model
8.2 Count Data Model
8.3 Nonparametric Models

9. Miscellaneous Topics

9.1 Quantile Regression Analysis
9.2 Simulation Methods
9.3 Data with Multilevel Structures
9.4 Errors of Measurement
9.5 Estimating Distributed Lags in Short Panels
9.6 Rotating or Randomly Missing Data
9.7 Pseudo Panels (or Repeated Cross-Sectional Data)
9.8 Discretizing Unobserved Heterogeneity

10. Interactive Effects Models

10.1 Introduction
10.2 Fixed-Effects Linear Static Models
10.3 Fixed-Effects Linear Dynamic Models
10.4 Models with Mixed Fixed and Random Effects
10.5 Quantile Models
10.6 Factor Dimension Determination

11. Spatial Models and Tests for Cross-Sectional Dependence

11.1 Introduction– Weak or Strong Cross–Correlation
11.2 The Basic Formulation on Spatial Weight Matrix and Spatial (Dependence) Coefficients
11.3 Spatial Error Models
11.4 Spatial Regressive Models
11.5 Some Extensions
11.6 Mixed Spatial and Factor Process
11.7 Cross-Sectional Dependence Test

12. Program Evaluation Using Panel Data

12.1 Introduction
12.2 Cross-Sectional Data Approach
12.3 Panel Data Approach
12.4 Some Examples
12.5 Multiple Treated Units
12.6 Simulating or Ranking Program Outcomes with Different Policy Options

13. Variable Coefficients Models

13.1 Introduction
13.2 Coefficients that Vary over Cross-Sectional Units
13.3 Coefficients that Vary over Time and Cross-Sectional Units
13.4 A Mixed Fixed- and Random-Coefficients Model
13.5 Dynamic Random-Coefficients Models
13.6 An Example: Liquidity Constraints and Firm Investment Expenditure
13.7 Coefficients that Vary over Time
13.8 Coefficients that Are Functions of Other Exogenous Variables
13.9 Correlated Random-Coefficients Models
Appendix 13A: Combination of Two Normal Distributions

14. Big Data Analysis

14.1 Machine Learning Algorithms
14.2 Inference with High-Dimensional Data
14.3 Inference for Low-Dimensional Parameters in the Presence of High-Dimensional Data
14.4 Prediction

References

Author Index

Subject Index

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