Econometric Analysis of Panel Data, Sixth Edition, by Badi H. Baltagi, is a standard reference for performing estimation and inference on panel datasets from an econometric standpoint. This book provides both a rigorous introduction to standard panel estimators and concise explanations of many newer, 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.
The sixth edition has been substantially updated to reflect modern developments in panel-data analysis. This edition also includes new material on dynamic panels, limited dependent variables, nonstationary panels, and spatial panel data.
Because of its wide range of topics and detailed exposition, Econometric Analysis of Panel Data, Sixth Edition can serve as both a graduate-level textbook and a handy desk reference for seasoned researchers.
1.1.2 Examples of Macro-panels
1.1.3 Some Basic References
1.2 Why Should We Use Panel Data? Their Benefits and Limitations
1.3 Note
References
2.2 The One-Way Fixed Effects Model
2.3 The One-Way Random Effects Model
2.4 Maximum Likelihood Estimation
2.5 Prediction
2.6 Examples
2.6.2 Example 2: Gasoline Demand Equation
2.6.3 Example 3: Public Capital Productivity
2.7 Selected Applications
2.8 Computational Note
2.9 Notes
2.10 Problems
References
3.2 The Two-Way Fixed Effects Model
3.3 The Two-Way Random Effects Model
3.4 Maximum Likelihood Estimation
3.5 Prediction
3.6 Examples
3.6.2 Example 2: Gasoline Demand Equation
3.6.3 Example 3: Public Capital Productivity
3.7 Computational Note
3.8 Notes
3.9 Problems
References
4.1.2 Test for Poolability Under the General Assumption u~N(0,Ω)
4.1.3 Examples
4.2 Tests for Individual and Time Effects
4.2.2 Honda, King and Wu, and the Standardized Lagrange Multiplier Tests
4.2.3 Gourieroux, Holly and Monfort Test
4.2.4 Conditional LM Tests
4.2.5 ANOVA F and the Likelihood Ratio Tests
4.2.6 Monte Carlo Results
4.2.7 An Illustrative Example
4.3 Hausman’s Specification Test
4.3.2 Example 2: Gasoline Demand Equation
4.3.3 Example 3: Canadian Manufacturing Industries
4.3.4 Example 4: Sawmills in Washington State
4.3.5 Example 5: Mariage Premium
4.3.6 Example 6: Currency Union
4.3.7 Hausman’s Test for the Two-Way Model
4.4 Further Reading
4.5 Notes
4.6 Problems
References
5.2 Serial Correlation
5.2.2 The AR(2) Process
5.2.3 The AR(4) Process for Quarterly Data
5.2.4 The MA(1) Process
5.2.5 Unequally Spaced Panels with AR(1) Disturbances
5.2.6 Prediction
5.2.7 Testing for Serial Correlation and Individual Effects
5.3 Time-Wise Autocorrelated and Cross-Sectionally Heteroskedastic Panel Regression
5.4 Further Reading
5.5 Notes
5.6 Problems
References
6.2 The Two-Way Model
6.3 Applications and Extensions
6.4 Problems
References
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
7.7 Notes
7.8 Problems
References
8.2 The Arellano and Bond Estimator
8.2.2 Downward Bias of the Estimated Asymptotic Standard Errors
8.2.3 Too Many Moment Conditions and the Bias Efficiency Trade-Off
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 Limited Information Maximum Likelihood
8.8 Empirical Examples
8.8.2 Example 2: Democracy and Education
8.9 Selected Applications
8.10 Further Reading
8.11 Notes
8.12 Problems
References
9.2 The Unbalanced One-Way Error Component Model
9.3 Maximum Likelihood Estimators
9.3.2 Monte Carlo Results
9.4 Empirical Example: Hedonic Housing
9.5 The Unbalanced Two-Way Error Component Model
9.5.2 The Random Effects Model
9.6 Testing for Individual and Time Effects Using Unbalanced Panel Data
9.7 The Unbalanced Nested Error Component Model
9.8 Notes
9.9 Problems
References
10.2 Rotating Panels
10.3 Pseudo-Panels
10.4 Short-Run versus Long-Run Estimates in Pooled Models
10.5 Heterogeneous Panels
10.6 Count Panel Data
10.7 Notes
10.8 Problems
References
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
11.9 Notes
11.10 Problems
References
12.2 Panel Unit Roots Tests Assuming Cross-Sectional Independence
12.2.2 Im, Pesaran and Shin Test
12.2.3 Breitung’s Test
12.2.4 Combining p-Value Tests
12.2.5 Residual-Based LM Test
12.3 Panel Unit Roots Tests Allowing for Cross-Sectional Dependence
12.4 Spurious Regression in Panel Data
12.5 Panel Cointegration Tests
12.5.2 Residual-Based LM Test
12.5.3 Pedroni Tests
12.5.4 Likelihood-Based Cointegration Test
12.5.5 Finite Sample Properties
12.6 Estimation and Inference in Panel Cointegration Models
12.7 Empirical Examples
12.7.2 Example 2: International R&D Spillover
12.7.3 Example 3: OECD Health Care Expenditures
12.8 Further Reading
12.9 Notes
12.10 Problems
References
13.2 Spatial Error Component Regression Model
13.3 Spatial Lag Panel Data Regression Model
13.4 Forecasts using Panel Data with Spatial Error Correlation
13.5 Panel Unit Root Tests and Spatial Dependence
13.6 Panel Data Tests for Cross-Sectional Dependence
13.7 Computational Note
13.8 Problems
