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Longitudinal and Panel Data: Analysis and Applications in the Social Sciences

Author:
Edward W. Frees
Publisher: Cambridge University Press
Copyright: 2004
ISBN-13: 978-0-521-53538-0
Pages: 467; paperback
Price: $46.50

Comment from the Stata technical group

Longitudinal and Panel Data is among the most complete of texts devoted to panel-data methodology and, although it is organized as a graduate text, will also serve as a valuable reference. Since the technology associated with analyzing panel data is quickly evolving, it is important to have such a current survey of the various models and estimation techniques available.

Beginning with linear fixed-effects models, the discussion proceeds naturally toward linear random-effects models and linear mixed models (including details on estimation, prediction, and inference). Instrumental variables and dynamic-panel models are then covered, followed by chapters covering binary dependent variables, generalized linear models, and categorical/survival models. One of the biggest strengths of this text is its detailed discussion of the various estimation methods and how they apply to the various models: maximum likelihood, restricted maximum likelihood, generalized estimating equations, generalized method of moments, and Kalman filters, just to name a few.


Table of contents

Preface
1 Introduction
1.1 What Are Longitudinal and Panel Data?
1.2 Benefits and Drawbacks of Longitudinal Data
1.3 Longitudinal Data Models
1.4 Historical Notes
2 Fixed-Effects Models
2.1 Basic Fixed-Effects Model
2.2 Exploring Longitudinal Data
2.3 Estimation and Inference
2.4 Model Specification and Diagnostics
2.5 Model Extensions
Further Reading
Appendix 2A. Least-Squares Estimation
Exercises and Extensions
3 Models with Random Effects
3.1 Error-Components/Random-Intercepts Model
3.2 Example: Income Tax Payments
3.3 Mixed-Effects Models
3.4 Inference for Regression Coefficients
3.5 Variance Components Estimation
Further Reading
Appendix 3A. REML Calculations
Exercises and Extensions
4 Prediction and Bayesian Inference
4.1 Estimators versus Predictors
4.2 Predictions for One-Way ANOVA Models
4.3 Best Linear Unbiased Predictors
4.4 Mixed-Model Predictors
4.5 Example: Forecasting Wisconsin Lottery Sales
4.6 Bayesian Inference
4.7 Credibility Theory
Further Reading
Appendix 4A. Linear Unbiased Prediction
Exercises and Extensions
5 Multilevel Models
5.1 Cross-Sectional Multilevel Models
5.2 Longitudinal Multilevel Models
5.3 Prediction
5.4 Testing Variance Components
Further Reading
Appendix 5A. High-Order Multilevel Models
Exercises and Extensions
6 Stochastic Regressors
6.1 Stochastic Regressors in Nonlongitudinal Settings
6.2 Stochastic Regressors in Longitudinal Settings
6.3 Longitudinal Data Models with Heterogeneity Terms and Sequentially Exogenous Regressors
6.4 Multivariate Responses
6.5 Simultaneous-Equations Models with Latent Variables
Further Reading
Appendix 6A. Linear Projections
7 Modeling Issues
7.1 Heterogeneity
7.2 Comparing Fixed- and Random-Effects Estimators
7.3 Omitted Variables
7.4 Sampling, Selectivity Bias, and Attrition
Exercises and Extensions
8 Dynamic Models
8.1 Introduction
8.2 Serial Correlation Models
8.3 Cross-Sectional Correlations and Time-Series Cross-Section Models
8.4 Time-Varying Coefficients
8.5 Kalman Filter Approach
8.6 Example: Capital Asset Pricing Model
Appendix 8A. Inference for the Time-Varying
Coefficient Model
9 Binary Dependent Variables
9.1 Homogeneous Models
9.2 Random-Effects Models
9.3 Fixed-Effects Models
9.4 Marginal Models and GEE
Further Reading
Appendix 9A. Likelihood Calculations
Exercises and Extensions
10 Generalized Linear Models
10.1 Homogeneous Models
10.2 Example: Tort Filings
10.3 Marginal Models and GEE
10.4 Random-Effects Models
10.5 Fixed-Effects Models
10.6 Bayesian Inference
Further Reading
Appendix 10A. Exponential Families of Distributions
Exercises and Extensions
11 Categorical Dependent Variables and Survival Models
11.1 Homogeneous Models
11.2 Multinomial Logit Models with Random-Effects
11.3 Transition (Markov) Models
11.4 Survival Models
Appendix 11A. Conditional Likelihood Estimation for Multinomial Logit Models with Heterogeneity Terms
Appendix A Elements of Matrix Algebra
A.1 Basic Terminology
A.2 Basic Operations
A.3 Additional Definitions
A.4 Matrix Decompositions
A.5 Partitioned Matrices
A.6 Kronecker (Direct) Product
Appendix B Normal Distribution
B.1 Univariate Normal Distribution
B.2 Multivariate Normal Distribution
B.3 Normal Likelihood
B.4 Conditional Distributions
Appendix C Likelihood-Based Inference
C.1 Characteristics of Likelihood Functions
C.2 Maximum Likelihood Estimators
C.3 Iterated Reweighted Least Squares
C.4 Profile Likelihood
C.5 Quasi-Likelihood
C.6 Estimating Equations
C.7 Hypothesis Tests
C.8 Goodness-of-Fit Statistics
C.9 Information Criteria
Appendix D State Space Model and the Kalman Filter
D.1 Basic State Space Model
D.2 Kalman Filter Algorithm
D.3 Likelihood Equations
D.4 Extended State Space Model and Mixed Linear Models
D.5 Likelihood Equations for Mixed Linear Models
Appendix E Symbols and Notation
Appendix F Selected Longitudinal and Panel Data Sets
References
Index
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