Stata Bookstore: Econometric Analysis of Panel Data, Sixth Edition

» Home » Bookstore » Title index » Econometrics » Econometric Analysis of Panel Data, Sixth Edition

Econometric Analysis of Panel Data, Sixth Edition

As an Amazon Associate, StataCorp earns a small referral credit from qualifying purchases made from affiliate links on our site.

Amazon Associate affiliate link

What are VitalSource eBooks?
Your access code will be emailed upon purchase.

VitalSource eBook details

VitalSource eBooks are read using the Bookshelf® platform. Bookshelf is free and allows you to access your Stata Press eBook from your computer, smartphone, tablet, or eReader.

How to access your eBook

1) Visit Bookshelf online to sign in or create an account.

2) Once logged in, click redeem in the upper right corner. Enter your eBook code. Your eBook code will be in your order confirmation email under the eBook's title.

3) The eBook will be added to your library. You may then download Bookshelf on other devices and sync your library to view the eBook.

Bookshelf is available on the following:

Online
Bookshelf is available online from just about any Internet-connected computer by accessing https://online.vitalsource.com/user/new.
PC
Bookshelf is available for Windows 7/8/8.1/10 (both 32-, and 64-bit).
Download Bookshelf software to your desktop so you can view your eBooks with or without Internet access.
iOS
Bookshelf is available for iPad, iPhone, and iPod touch.
Download the Bookshelf mobile app from the Itunes Store.
Android
Bookshelf is available for Android phones and tablets running 4.0 (Ice Cream Sandwich) and later.
Download the Bookshelf mobile app from the Google Play Store.
Kindle Fire
Bookshelf is available for Kindle Fire 2, HD, and HDX.
Download the Bookshelf mobile app from the Kindle Fire App Store.
Mac
Bookshelf is available for macOS X 10.9 or later.
Download Bookshelf software to your desktop so you can view your eBooks with or without Internet access.

Bookshelf allows you to have 2 computers and 2 mobile devices activated at any given time.

Learn more about VitalSource Bookshelf »

Resources and support »

I was amazed at the VitalSource way of presenting the books.... Everything looks perfectly typeset, but yet you can "flip" through the book in the same way you would "flip" through a very long web page in your web browser. And best of all, whenever I have my tablet with me, my books are just a swipe away.

— Michael Mitchell

Senior statistician at the USC Children's Data Network, author of four Stata Press books, and former UCLA statistical consultant who envisioned and designed the UCLA Statistical Consulting Resources website.

Return policy for eBooks

Stata Press eBooks are nonreturnable and nonrefundable.

eBook not available for this title

Author:	Badi H. Baltagi
Publisher:	Springer
Copyright:	2021
ISBN-13:	978-3-030-53952-8
Pages:	444; paperback

Author:	Badi H. Baltagi
Publisher:	Springer
Copyright:	2021
ISBN-13:
Pages:	444; eBook

Author:	Badi H. Baltagi
Publisher:	Springer
Copyright:	2021
ISBN-13:
Pages:	444; Kindle

Comment from the Stata technical group

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.

View table of contents >>

Table of Contents

Preface

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
References

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
References

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
References

4 Test of Hypotheses with Panel Data

4.1 Tests for Poolability

4.1.1 Test for Poolability u~N(0,σ²I_NT)
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
References

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
References

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
References

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
References

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
References

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
References

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
References

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
References

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
References

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
References