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Econometric Analysis of Panel Data, 4th Edition
Author: Badi H. Baltagi
Publisher: Wiley
Copyright: 2008
ISBN-10: 0-470-51886-3
ISBN-13: 978-0-470-51886-1
Pages: 366; paperback
Price: $59.00
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Table of contents
Comment from the Stata technical group
If you need to know how to perform estimation and inference on panel data from an econometric standpoint, then Econometric Analysis of Panel Data, Fourth Edition by Badi H. Baltagi is the book to read. Aside from being a leading graduate textbook, this book is the standard reference, containing all the details you need to understand and implement the standard models. It also provides a very good introduction to the newer and more advanced techniques.
This book provides an excellent introduction for the student or the applied researcher because of its attention to detail and its use of examples, many of which use Stata. The detail is especially useful in the many sections that grow out of Baltagi’s own work. In these sections, readers gain a deep enough understanding of the models to implement them in a programming language like Stata. In other sections, such as the chapter on limited dependent variables, Baltagi combines a good introduction to the mechanics with an excellent introduction to the literature, allowing readers the opportunity to follow up for more details.
This fourth edition updates the coverage of recent theoretical developments. There is a new section on count panel data that links a detailed but intuitive theoretical discussion with examples using the Stata commands xtpoisson and xtnbreg. Baltagi has also considerably expanded the section on dynamic panel-data methods and has included Stata examples.
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Table of contents
Preface
1. Introduction
1.1 Panel Data: Some Examples
1.2 Why Should We Use Panel Data? Their Benefits and Limitations
Note
2. The One-way Error Component Regression Model
2.1 Introduction
2.2 The Fixed Effects Model
2.3 The Random Effects Model
2.4 Maximum Likelihood Estimation
2.5 Prediction
2.6 Examples
2.7 Selected Applications
2.8 Computational Note
Notes
Problems
3. The Two-way Error Component Regression Model
3.1 Introduction
3.2 The Fixed Effects Model
3.3 The Random Effects Model
3.4 Maximum Likelihood Estimation
3.5 Prediction
3.6 Examples
3.7 Selected Applications
Notes
Problems
4. Test of Hypotheses with Panel Data
4.1 Tests for Poolability of the Data
4.2 Tests for Individual and Time Effects
4.3 Hausman’s Specification Test
4.4 Further Reading
Notes
Problems
5. Heterskedasticity and Serial Correlation in the Error Component Model
5.1 Heteroskedasticity
5.2 Serial Correlation
Notes
Problems
6. Seemingly Unrelated Regressions with Error Components
6.1 The One-way Model
6.2 The Two-way Model
6.3 Applications and Extensions
Problems
7. Simultaneous Equations with Error Components
7.1 Single Equation Estimation
7.2 Empirical Example: Crime in North Carolina
7.3 System Estimation
7.4 The Hausman and Taylor Estimator
7.5 Empirical Example: Earnings Equation Using PSID Data
7.6 Further Reading and Extensions
Notes
Problems
8. Dynamic Panel Data Models
8.1 Introduction
8.2 The Arellano and Bond Estimator
8.3 The Arellano and Bover Estimator
8.4 The Ahn and Schmidt Moment Conditions
8.5 The Blundell and Bond System GMM Estimator
8.6 The Keane and Runkle Estimator
8.7 Further Developments
8.8 Empirical Examples
8.9 Further Reading
Notes
Problems
9. Unbalanced Panel Data Models
9.1 Introduction
9.2 The Unbalanced One-way Error Component Model
9.3 Empirical Example: Hedonic Housing
9.4 The Unbalanced Two-way Error Component Model
9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data
9.6 The Unbalanced Nested Error Component Model
Notes
Problems
10. Special Topics
10.1 Measurement Error and Panel Data
10.2 Rotating Panels
10.3 Pseudo-panels
10.4 Alternative Methods of Pooling Time Series of Cross-section Data
10.5 Spatial Panels
10.6 Short-run vs Long-run Estimates in Pooled Models
10.7 Heterogeneous Panels
10.8 Count Panel Data
Notes
Problems
11. Limited Dependent Variables and Panel Data
11.1 Fixed and Random Logit and Probit Models
11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data
11.3 Dynamic Panel Data Limited Dependent Variable Models
11.4 Selection Bias in Panel Data
11.5 Censored and Truncated Panel Data Models
11.6 Empirical Applications
11.7 Empirical Example: Nurses Labor Supply
11.8 Further Reading
Notes
Problems
12. Nonstationary Panels
12.1 Introduction
12.2 Panel Unit Root Tests Assuming Cross-sectional Independence
12.3 Panel Unit Root Tests Allowing for Cross-sectional Dependence
12.4 Spurious Regression in Panel Data
12.5 Panel Cointegration Tests
12.6 Estimation and Inference in Panel Cointegration Models
12.7 Empirical Example: Purchasing Power Parity
12.8 Further Reading
Notes
Problems
References
Index
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