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, Specifi cation, 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.