• 1.1 Introduction    1
• 1.2 Distributions, Densities, and Moments    3
• 1.3 The Specification of Regression Models    15
• 1.4 Matrix Algebra    22
• 1.5 Method-of-Moments Estimation    30
• 1.6 Notes on the Exercises    37

Chapter 2   The Geometry of Linear Regression    42

• 2.1 Introduction    42
• 2.2 The Geometry of Vector Spaces    43
• 2.3 The Geometry of OLS Estimation    54
• 2.4 The Frisch-Waugh-Lovell Theorem    62
• 2.5 Applications of the FWL Theorem    69
• 2.6 Influential Observations and Leverage    76
• 2.7 Final Remarks    81

Chapter 3   The Statistical Properties of Ordinary Least Squares    86

• 3.1 Introduction    86
• 3.2 Are OLS Parameter Estimators Unbiased?    88
• 3.3 Are OLS Parameter Estimators Consistent?    92
• 3.4 The Covariance Matrix of the OLS Parameter Estimates    97
• 3.5 Efficiency of the OLS Estimator    104
• 3.6 Residuals and Error Terms    107
• 3.7 Misspecification of Linear Regression Models    111
• 3.8 Measures of Goodness of Fit    115
• 3.9 Final Remarks    118

Chapter 4   Hypothesis Testing in Linear Regression Models    122

• 4.1 Introduction    122
• 4.2 Basic Ideas    122
• 4.3 Some Common Distributions    129
• 4.4 Exact Tests in the Classical Normal Linear Model    138
• 4.5 Large-Sample Tests in Linear Regression Models    146
• 4.6 Simulation-Based Tests    155
• 4.7 The Power of Hypothesis Tests    166
• 4.8 Final Remarks    172

Chapter 5   Confidence Intervals    177

• 5.1 Introduction    177
• 5.2 Exact and Asymptotic Confidence Intervals    178
• 5.3 Bootstrap Confidence Intervals    185
• 5.4 Confidence Regions    189
• 5.5 Heteroskedasticity-Consistent Covariance Matrices    196
• 5.6 The Delta Method    202
• 5.7 Final Remarks    209

Chapter 6   Nonlinear Regression    213

• 6.1 Introduction    213
• 6.2 Method-of-Moments Estimators for Nonlinear Models    215
• 6.3 Nonlinear Least Squares    224
• 6.4 Computing NLS Estimates    228
• 6.5 The Gauss-Newton Regression    235
• 6.6 One-Step Estimation    240
• 6.7 Hypothesis Testing    243
• 6.8 Heteroskedasticity-Robust Tests    250
• 6.9 Final Remarks    253

Chapter 7   Generalized Least Squares and Related Topics    257

• 7.1 Introduction    257
• 7.2 The GLS Estimator    258
• 7.3 Computing GLS Estimates    260
• 7.4 Feasible Generalized Least Squares    264
• 7.5 Heteroskedasticity    266
• 7.6 Autoregressive and Moving-Average Processes    270
• 7.7 Testing for Serial Correlation    275
• 7.8 Estimating Models with Autoregressive Errors    285
• 7.9 Specification Testing and Serial Correlation    292
• 7.10 Models for Panel Data    298
• 7.11 Final Remarks    305