Econometric Analysis of Panel Data

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.

Table of Contents


1 Introduction
1.1 Panel Data: Some Examples

1.1.1 Examples of Micro-panels
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


2 The One-Way Error Component Regression Model
2.1 Introduction
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

2.6.1 Example 1: Investment Equation
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


3 The Two-Way Error Component Regression Model
3.1 Introduction
3.2 The Two-Way Fixed Effects Model

3.2.1 Testing for Fixed Effects

3.3 The Two-Way Random Effects Model

3.3.1 Monte Carlo Results

3.4 Maximum Likelihood Estimation
3.5 Prediction
3.6 Examples

3.6.1 Example 1: Investment Equation
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


4 Test of Hypotheses with Panel Data
4.1 Tests for Poolability

4.1.1 Test for Poolability u~N(0,σ2INT)
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

4.2.1 The Breusch—Pagan Test
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

4.3.1 Example 1: Investment Equation
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


5 Heteroskedasticity and Serial Correlation in the Error Component Model
5.1 Heteroskedasticity

5.1.1 Testing for Homoskedasticity in an Error Component Model

5.2 Serial Correlation

5.2.1 The AR(1) Process
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


6 Seemingly Unrelated Regressions with Error Components
6.1 The One-Way Model
6.2 The Two-Way Model
6.3 Applications and Extensions
6.4 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
7.7 Notes
7.8 Problems


8 Dynamic Panel Data Models
8.1 Introduction
8.2 The Arellano and Bond Estimator

8.2.1 Testing for Over-Indentification Restrictions and Serial Correlation in Dynamic Panel Models
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

8.8.1 Example 1: Dynamic Demand for Cigarettes
8.8.2 Example 2: Democracy and Education

8.9 Selected Applications
8.10 Further Reading
8.11 Notes
8.12 Problems


9 Unbalanced Panel Data Models
9.1 Introduction
9.2 The Unbalanced One-Way Error Component Model

9.2.1 ANOVA Methods

9.3 Maximum Likelihood Estimators

9.3.1 Minimum Norm and Minimum Variance Quadratic Unbiased Estimators (MINQUE and MIVQUE)
9.3.2 Monte Carlo Results

9.4 Empirical Example: Hedonic Housing
9.5 The Unbalanced Two-Way Error Component Model

9.5.1 The Fixed Effects 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

9.7.1 Empirical Example: Nested States Public Capital Productivity

9.8 Notes
9.9 Problems


10 Special Topics
10.1 Measurement Error and Panel Data
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


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
11.9 Notes
11.10 Problems


12 Nonstationary Panels
12.1 Introduction
12.2 Panel Unit Roots Tests Assuming Cross-Sectional Independence

12.2.1 Levin, Lin and Chu Test
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

12.5.1 Residual-Based DF and ADF Tests (Kao 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

12.7.1 Example 1: Purchasing Power Parity
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


13 Spatial Panel Data Models
13.1 Introduction
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


Author: Badi H. Baltagi
Edition: Sixth Edition
ISBN-13: 978-3-030-53952-8
©Copyright: 2021 Springer

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.