Table Of Contents:

• List of Figures
• Series Editor’s Introduction
• Preface
• About the Author
• Acknowledgments
• Chapter 1: The Linear Regression Model (LRM)
• 1.1 Introduction
• 1.2 Meaning of “Linear” in Linear Regression
• 1.3 Estimation of the LRM: An Algebraic Approach
• 1.4 Goodness of Fit of a Regression Model: The Coefficient of Determination (R2)
• 1.5 R2 for Regression Through the Origin
• 1.6 An Example: The Determination of the Hourly Wages in the United States
• 1.7 Summary
• Exercises
• Appendix 1A: Derivation of the Normal Equations
• Chapter 2: The Classical Linear Regression Model (CLRM)
• 2.1 Assumptions of the CLRM
• 2.2 The Sampling or Probability Distributions of the OLS Estimators
• 2.3 Properties of OLS Estimators: The Gauss–Markov Theorem
• 2.4 Estimating Linear Functions of the OLS Parameters
• 2.5 Large-Sample Properties of OLS Estimators
• 2.6 Summary
• Exercises
• Chapter 3: The Classical Normal Linear Regression Model: The Method of Maximum Likelihood (ML)
• 3.1 Introduction
• 3.2 The Mechanics of ML
• 3.3 The Likelihood Function of the k-Variable Regression Model
• 3.4 Properties of the ML Method
• 3.5 Summary
• Exercises
• Appendix 3A: Asymptotic Efficiency of the ML Estimators of the LRM
• Chapter 4: Linear Regression Model: Distribution Theory and Hypothesis Testing
• 4.1 Introduction
• 4.2 Types of Hypotheses
• 4.3 Procedure for Hypothesis Testing
• 4.4 The Determination of Hourly Wages in the United States
• 4.5 Testing Hypotheses About an Individual Regression Coefficient
• 4.6 Testing the Hypothesis That All the Regressors Collectively Have No Influence on the Regressand
• 4.7 Testing the Incremental Contribution of a Regressor
• 4.8 Confidence Interval for the Error Variance s 2
• 4.9 Large-Sample Tests of Hypotheses
• 4.10 Summary
• Exercises
• Appendix 4A: Constrained Least Squares: OLS Estimation Under Linear Restrictions
• Chapter 5: Generalized Least Squares (GLS): Extensions of the Classical Linear Regression Model
• 5.1 Introduction
• 5.2 Estimation of B With a Nonscalar Covariance Matrix
• 5.3 Estimated Generalized Least Squares
• 5.4 Heteroscedasticity and Weighted Least Squares
• 5.5 White’s Heteroscedasticity-Consistent Standard Errors
• 5.6 Autocorrelation
• 5.7 Summary
• Exercises
• Appendix 5A: ML Estimation of GLS
• Chapter 6: Extensions of the Classical Linear Regression Model: The Case of Stochastic or Endogenous Regressors
• 6.1 Introduction
• 6.2 X and u Are Distributed Independently
• 6.3 X and u Are Contemporaneously Uncorrelated
• 6.4 X and u Are Neither Independently Distributed Nor Contemporaneously Uncorrelated
• 6.5 The Case of k Regressors
• 6.6 What Is the Solution? The Method of Instrumental Variables (IVs)
• 6.7 Hypothesis Testing Under IV Estimation
• 6.8 Practical Problems in the Application of the IV Method
• 6.9 Regression Involving More Than One Endogenous Regressor
• 6.10 An Illustrative Example: Earnings and Educational Attainment of Youth in the United States
• 6.11 Regression Involving More Than One Endogenous Regressor
• 6.12 Summary
• Appendix 6A: Properties of OLS When Random X and u Are Independently Distributed
• Appendix 6B: Properties of OLS Estimators When Random X and u Are Contemporaneously Uncorrelated
• Chapter 7: Selected Topics in Linear Regression
• 7.1 Introduction
• 7.2 The Nature of Multicollinearity
• 7.3 Model Specification Errors
• 7.4 Qualitative or Dummy Regressors
• 7.5 Nonnormal Error Term
• 7.6 Summary
• Exercises
• Appendix 7A: Ridge Regression: A Solution to Perfect Collinearity
• Appendix 7B: Specification Errors
• Appendix A: Basics of Matrix Algebra
• A.1 Definitions
• A.2 Types of Matrices
• A.3 Matrix Operations
• A.4 Matrix Transposition
• A.5 Matrix Inversion
• A.6 Determinants
• A.7 Rank of a Matrix
• A.8 Finding the Inverse of a Square Matrix
• A.9 Trace of a Square Matrix
• A.10 Quadratic Forms and Definite Matrices
• A.11 Eigenvalues and Eigenvectors
• A.12 Vector and Matrix Differentiation
• Appendix B: Essentials of Large-Sample Theory
• B.1 Some Inequalities
• B.2 Types of Convergence
• B.3 The Order of Magnitude of a Sequence
• B.4 The Order of Magnitude of a Stochastic Sequence
• Appendix C: Small- and Large-Sample Properties of Estimators
• C.1 Small-Sample Properties of Estimators
• C.2 Large-Sample Properties of Estimators
• Appendix D: Some Important Probability Distributions
• D.1 The Normal Distribution and the Z Test
• D.2 The Gamma Distribution
• D.3 The Chi-Square (? 2) Distribution and the ? 2 Test
• D.4 Student’s t Distribution
• D.5 Fisher’s F Distribution
• D.6 Relationships Among Probability Distributions
• D.7 Uniform Distributions
• D.8 Some Special Features of the Normal Distribution
• Index