This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews.
Written by one of the world’s leading researchers and authors in the field, Econometric Analysis of Panel Data has established itself as the leading textbook for graduate and postgraduate courses on panel data. It provides up-to-date coverage of basic panel data techniques, illustrated with real economic applications and datasets, which are available at the book’s website on springer.com.
This new sixth edition has been fully revised and updated, and includes new material on dynamic panels, limited dependent variables and nonstationary panels, as well as spatial panel data. The author also provides empirical illustrations and examples using Stata and EViews.
“This is a definitive book written by one of the architects of modern, panel data econometrics. It provides both a practical introduction to the subject matter, as well as a thorough discussion of the underlying statistical principles without taxing the reader too greatly."
Professor Kajal Lahiri, State University of New York, Albany, USA. "This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference."
Professor Peter Schmidt, Michigan State University, USA.
“Panel data econometrics is in its ascendancy, combining the power of cross section averaging with all the subtleties of temporal and spatial dependence. Badi Baltagi provides a remarkable roadmap of this fascinating interface of econometric method, enticing the novitiate with technical gentleness, the expert with comprehensive coverage and the practitioner with many empirical applications.”
Professor Peter C. B. Phillips, Cowles Foundation, Yale University, USA.
Author(s): Badi H. Baltagi
Series: Springer Texts in Business and Economics
Edition: 6
Publisher: Springer
Year: 2021
Language: English
Pages: 444
City: Cham
Preface
References
Contents
List of Figures
List of Tables
Part I
1 What Is Econometrics?
1.1 Introduction
1.2 A Brief History
1.3 Critiques of Econometrics
1.4 Looking Ahead
Notes
References
2 Basic Statistical Concepts
2.1 Introduction
2.2 Methods of Estimation
2.3 Properties of Estimators
2.4 Hypothesis Testing
2.5 Confidence Intervals
2.6 Descriptive Statistics
Notes
Problems
Appendix
References
3 Simple Linear Regression
3.1 Introduction
3.2 Least Squares Estimation and the Classical Assumptions
3.3 Statistical Properties of Least Squares
3.4 Estimation of σ2
3.5 Maximum Likelihood Estimation
3.6 A Measure of Fit
3.7 Prediction
3.8 Residual Analysis
3.9 Numerical Example
3.10 Empirical Example
Problems
Appendix: Centered and Uncentered R2
References
4 Multiple Regression Analysis
4.1 Introduction
4.2 Least Squares Estimation
4.3 Residual Interpretation of Multiple Regression Estimates
4.4 Overspecification and Underspecification of the Regression Equation
4.5 R-Squared Versus R-Bar-Squared
4.6 Testing Linear Restrictions
4.7 Dummy Variables
Notes
Problems
Appendix: Residual Interpretation of Multiple Regression Estimates
References
5 Violations of the Classical Assumptions
5.1 Introduction
5.2 Normality of the Disturbances
5.3 Heteroskedasticity
5.4 Autocorrelation
Notes
Problems
References
6 Distributed Lags and Dynamic Models
6.1 Introduction
6.2 Infinite Distributed Lag
6.2.1 Adaptive Expectations Model (AEM)
6.2.2 Partial Adjustment Model (PAM)
6.3 Estimation and Testing of Dynamic Models with Serial Correlation
6.3.1 A Lagged Dependent Variable Model with AR(1) Disturbances
6.3.2 A Lagged Dependent Variable Model with MA(1) Disturbances
6.4 Autoregressive Distributed Lag
Notes
Problems
References
Part II
7 The General Linear Model: The Basics
7.1 Introduction
7.2 Least Squares Estimation
7.3 Partitioned Regression and the Frisch-Waugh-LovellTheorem
7.4 Maximum Likelihood Estimation
7.5 Prediction
7.6 Confidence Intervals and Test of Hypotheses
7.7 Joint Confidence Intervals and Test of Hypotheses
7.8 Restricted MLE and Restricted Least Squares
7.9 Likelihood Ratio, Wald and Lagrange Multiplier Tests
7.9.1 Chow1960:ch7's (Chow1960:ch7) Test for Regression Stability
7.9.2 The W, LR, and LM Inequality
Notes
Problems
Appendix: Some Useful Matrix Properties
References
8 Regression Diagnostics and Specification Tests
8.1 Influential Observations
8.2 Recursive Residuals
8.3 Specification Tests
8.4 Nonlinear Least Squares and the Gauss–Newton Regression
8.4.1 Diagnostic Tests for Linear Regression Models
8.5 Testing Linear Versus Log-Linear Functional Form
Notes
Problems
References
9 Generalized Least Squares
9.1 Introduction
9.2 Generalized Least Squares
9.2.1 Necessary and Sufficient Conditions for OLS to Be Equivalent to GLS
9.3 Special Forms of Ω
9.4 Maximum Likelihood Estimation
9.5 Test of Hypotheses
9.6 Prediction
9.7 Unknown Ω
9.8 The W, LR, and LM Statistics Revisited
9.9 Spatial Error Correlation
Notes
Problems
References
10 Seemingly Unrelated Regressions
10.1 Introduction
10.2 Feasible GLS Estimation
10.2.1 Relative Efficiency of OLS in the Case of Simple Regressions
10.2.2 Relative Efficiency of OLS in the Case of Multiple Regressions
10.3 Testing Diagonality of the Variance–Covariance Matrix
10.4 Seemingly Unrelated Regressions with Unequal Observations
10.5 Empirical Examples
Problems
References
11 Simultaneous Equations Model
11.1 Introduction
11.1.1 Simultaneous Bias
11.1.2 The Identification Problem
11.2 Single Equation Estimation: Two-Stage Least Squares
11.2.1 Spatial Lag Dependence
11.3 System Estimation: Three-Stage Least Squares
11.4 Test for Over-Identification Restrictions
11.5 Hausman's Specification Test
11.6 Empirical Examples
Notes
Problems
Appendix: The Identification Problem Revisited: The Rank Condition of Identification
References
12 Pooling Time-Series of Cross-Section Data
12.1 Introduction
12.2 The Error Components Model
12.2.1 The Fixed Effects Model
12.2.2 The Random Effects Model
12.2.3 Maximum Likelihood Estimation
12.3 Prediction
12.4 Empirical Example
12.5 Testing in a Pooled Model
12.6 Dynamic Panel Data Models
12.6.1 Empirical Illustration
12.7 Program Evaluation and Difference-in-Differences Estimator
12.7.1 The Difference-in-Differences Estimator
Problems
References
13 Limited Dependent Variables
13.1 Introduction
13.2 The Linear Probability Model
13.3 Functional Form: Logit and Probit
13.4 Grouped Data
13.5 Individual Data: Probit and Logit
13.6 The Binary Response Model Regression
13.7 Asymptotic Variances for Predictions and Marginal Effects
13.8 Goodness of Fit Measures
13.9 Empirical Examples
13.10 Multinomial Choice Models
13.10.1 Ordered Response Models
13.10.2 Unordered Response Models
13.11 The Censored Regression Model
13.12 The Truncated Regression Model
13.13 Sample Selectivity
13.14 Limited Dependent Variables Panel Data
Notes
Problems
Appendix
1. Truncated Normal Distribution
2. The Censored Normal Distribution
3. Sample Selection and Non-response
References
14 Time-Series Analysis
14.1 Introduction
14.2 Stationarity
14.3 The Box and Jenkins Method
14.4 Vector Autoregression
14.5 Unit Roots
14.6 Trend Stationary Versus Difference Stationary
14.7 Cointegration
14.8 A Cointegration Example
14.9 Autoregressive Conditional Heteroskedasticity
Notes
Problems
References
Appendix
Index
