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Econometric Analysis of Panel Data, Fifth Edition

Badi H. Baltagi
Publisher: Wiley
Copyright: 2013
ISBN-13: 978-1-118-67232-7
Pages: 390; paperback
Price: $64.50

Comment from the Stata technical group

Econometric Analysis of Panel Data, Fifth 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 as well as 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 fifth edition has been substantially updated to reflect modern developments in panel-data analysis. Virtually every chapter has been revised, and references have been updated to reflect the most recent developments in the literature. An entirely new chapter dealing with the analysis of spatial panel datasets has been added, and the chapters on dynamic panel-data models, limited dependent variables, and nonstationary panels have been significantly revised.

Because of its wide range of topics and detailed exposition, Econometric Analysis of Panel Data, Fifth Edition, can serve as both a graduate-level textbook as well as a handy desk reference for seasoned researchers.

Table of contents

Table of Contents
1 Introduction
1.1 Panel Data: Some Examples
1.2 Why Should We Use Panel Data? Their Benefits and Limitations
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
2.7 Selected Applications
2.8 Computational Note
3 The Two-way Error Component Regression Model
3.1 Introduction
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
3.7 Computational Note
4 Test of Hypotheses with Panel Data
4.1 Tests for Poolability
4.2 Tests for Individual and Time Effects
4.3 Hausman's Specification Test
4.4 Further Reading
5 Heteroskedasticity and Serial Correlation in the Error Component Model
5.1 Heteroskedasticity
5.2 Serial Correlation
5.3 Time-wise Autocorrelated and Cross-sectionally Heteroskedastic Panel Regression
5.4 Further Reading
6 Seemingly Unrelated Regressions with Error Components
6.1 The One-way Model
6.2 The Two-way Model
6.3 Applications and Extensions
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
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 Limited Information Maximum Likelihood
8.8 Further Developments
8.9 Empirical Examples
8.10 Selected Applications
8.11 Further Reading
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
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
11 Limited Dependent Variables and Panel Data
11.1 Introduction
11.2 Fixed and Random Logit and Probit Models
11.3 Simulation Estimation of Limited Dependent Variable Models with Panel Data
11.4 Dynamic Panel Data Limited Dependent Variable Models
11.5 Selection Bias in Panel Data
11.6 Censored and Truncated Panel Data Models
11.7 Applications
11.8 Empirical Example: Nurses’ Labour Supply
11.9 Further Reading
12 Nonstationary Panels
12.1 Introduction
12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence
12.3 Panel Unit Roots 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 Examples
12.8 Further Reading
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 Further Reading
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