Table of Contents

Introduction 1

Part I: Getting Started with Econometrics 5

Chapter 1: Econometrics: The Economist’s Approach to Statistical Analysis 7

Chapter 2: Getting the Hang of Probability 21

Chapter 3: Making Inferences and Testing Hypotheses 39

Part II: Building the Classical Linear Regression Model 59

Chapter 4: Understanding the Objectives of Regression Analysis 61

Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique 75

Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem 93

Chapter 7: The Normality Assumption and Inference with OLS 111

Part III: Working with the Classical Regression Model 135

Chapter 8: Functional Form, Specification, and Structural Stability 137

Chapter 9: Regression with Dummy Explanatory Variables 153

Part IV: Violations of Classical Regression Model Assumptions 173

Chapter 10: Multicollinearity 175

Chapter 11: Heteroskedasticity 191

Chapter 12: Autocorrelation 209

Part V: Discrete and Restricted Dependent Variables in Econometrics 229

Chapter 13: Qualitative Dependent Variables 231

Chapter 14: Limited Dependent Variable Models 253

Part VI: Extending the Basic Econometric Model 265

Chapter 15: Static and Dynamic Models 267

Chapter 16: Diving into Pooled Cross-Section Analysis 281

Chapter 17: Panel Econometrics 291

Part VII: The Part of Tens 305

Chapter 18: Ten Components of a Good Econometrics Research Project 307

Chapter 19: Ten Common Mistakes in Applied Econometrics 315

Appendix: Statistical Tables 321

Index 327

About the Author

Roberto Pedace, PhD, is an associate professor in the Department of Economics at Scripps College. His published work has appeared in Economic Inquiry, Industrial Relations, the Southern Economic Journal, Contemporary Economic Policy, the Journal of Sports Economics, and other outlets.