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Applied Panel Data Analysis for Economic and Social Surveys

Authors:
Hans-Jürgen Andreß, Katrin Golsch, and Alexander W. Schmidt
Publisher: Springer
Copyright: 2013
ISBN-13: 978-3-642-32913-5
Pages: 327; hardcover
Price: $49.75

Comment from the Stata technical group

Applied Panel Data Analysis for Economic and Social Surveys, by Hans-Jürgen Andreß, Katrin Golsch, and Alexander W. Schmidt, provides a rigorous yet intuitive discussion of panel-data methods as they are used in the social sciences. Because the book does not make use of matrix algebra or advanced econometric methods, it will appeal to masters-level students and practitioners as well as doctoral students and researchers. The book covers all commonly used estimation methods, including fixed- and random-effects estimators, first-difference estimators, and maximum likelihood estimators. Separate chapters consider continuous and categorical dependent variables. In contrast to many panel-data texts, Applied Panel Data Analysis for Economic and Social Surveys also considers discrete-time event-study (hazard) models and their relationship to other panel-data models. All the datasets used in the book are available in Stata format as well as Stata do-files to replicate all the examples in the text.


Table of contents

1 Introduction
1.1 Benefits and Challenges of the Panel Design
1.1.1 Benefits
1.1.2 Challenges
1.2 Outline of the Book
1.3 Audience and Prerequisites
2 Managing Panel Data
2.1 The Nature of Panel Data
2.2 The Basics of Panel Data Management
2.2.1 Merging and Appending Data
2.2.2 Basic Append and Merge Commands
2.2.3 Building a Working Data Set with Append and Merge Commands
2.2.4 Wide and Long Format
2.2.5 Some General Remarks
2.3 Three Case Studies on Poverty in Germany
2.3.1 Case Study 1: How Many German Citizens Were Poor in 2004?
A Cross-Sectional Analysis
2.3.2 Case Study 2: Did Poverty Increase in Germany After 2004?
An Analysis of Pooled Crossed-Sections
2.3.3 Case Study 3: How Large Is the Risk of Becoming Poor in Germany?
A Panel Analysis
2.4 How to Represent a Population with Panel Data?
2.4.1 Weighted and Unweighted Analysis of Cross-Sections
2.4.2 Weighting in Balanced and Unbalanced Panels
2.4.3 When to Use Weights?
2.5 Conclusion and Further Reading
3 Describing and Modeling Panel Data
3.1 Some Basic Terminology
3.2 Measurements over Time Are Not Independent
3.3 Describing the Dependent Variable
3.4 Explaining the Dependent Variable over Time: Typical Explanatory Variables
3.4.1 Time-Constant and Time-Varying Variables
3.4.2 Serially Correlated Observations
3.5 Modeling Panel Data
3.5.1 Modeling the Level of Dependent Variable
3.5.2 Modeling Change of the Dependent Variable
3.5.3 Additional Models
3.6 Estimating Models for Panel Data
3.6.1 Omitted Variable Bias (Unobserved Heterogeneity)
3.6.2 Serially Correlated and Heteroscedastic Errors
3.6.3 Measurement Error Bias
3.6.4 A Formal Summary of the Main Estimation Assumptions
3.7 Overview of Subsequent Chapters
4 Panel Analysis of Continuous Variables
4.1 Modeling the Level of Y
4.1.1 Ignoring the Panel Structure
4.1.2 Modeling the Panel Structure
4.1.3 Extensions
4.2 Modeling the Change of Y
4.2.1 Analysis of Change Using Change Scores
4.2.2 Analysis of Change Using Impact Functions
4.2.3 Analysis of Trends
4.3 Conclusion and Further Reading
5 Panel Analysis of Categorical Variables
5.1 Modeling the Level of Y: Discrete Response Models for Panel Data
5.1.1 Ignoring the Panel Structure
5.1.2 Modeling the Panel Structure
5.1.3 Extensions
5.2 Modeling the Change of Y: Discrete-Time Event History Models for Panel Data
5.2.1 Basic Terminology
5.2.2 How to Estimate a Discrete-Time Hazard Model
5.2.3 Applying the Discrete-Time Event History Model
5.2.4 Extensions
5.3 Conclusion and Further Reading
6 How to Do Your Own Panel Analysis
7 Useful Background Information
7.1 Functions of Random Variables
7.2 Estimation and Testing
7.2.1 Ordinary Least Squares
7.2.2 Maximum Likelihood
7.3 Web Site of the Textbook
References
Index
Author Index
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