Fundamentals of Applied Econometrics

Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools.

The text serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise.

Divided into two parts, the first half provides a thorough undergraduate-level treatment of multiple regressions including an extensive statistics review with integrated, hands-on Acting Learning Exercises so students learn by doing.

The second half of the book covers a number of advanced topics: panel data modeling, time series analysis, binary-choice modeling, and an introduction to GMM.

This latter portion of the book is very suitable for a more advanced course: a second-term undergraduate course, a Master?s level course, or as a companion reading for a Doctoral level course.

What’s Different about Thi' Book xiii

Working with Data in the "Active Learning Exercises" xxii

Acknowledgments xxiii

Notation xxiv

Part I. Introduction and Statistics Review 1

Chapter 1. Introduction 3

Chapter 2. A Review of Probability Theory 11

Chapter 3. Estimating the Mean of a Normally Distributed Random Variable 46

Chapter 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 68

Part II. Regression Analysis 97

Chapter 5. The Bivariate Regression Model: Introduction, Assumptions, and Parameter Estimates 99

Chapter 6. The Bivariate Linear Regression Model: Sampling Distributions and Estimator Properties 131

Chapter 7. The Bivariate Linear Regression Model: Inference on β 150

Chapter 8. The Bivariate Regression Model: R² and Prediction 178

Chapter 9. The Multiple Regression Model 191

Chapter 10. Diagnostically Checking and Respecifying the Multiple Regression Model: Dealing with Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 224

Chapter 11. Stochastic Regressors and Endogeneity 259

Chapter 12. Instrumental Variables Estimation 303

Chapter 13. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part A) 342

Chapter 14. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part B) 389

Part III. Additional Topics in Regression Analysis 455

Chapter 15. Regression Modeling with Panel Data (Part A) 459

Chapter 16. Regression Modeling with Panel Data (Part B) 507

Chapter 17. A Concise Introduction to Time-Series Analysis and Forecasting (Part A) 536

Chapter 18. A Concise Introduction to Time-Series Analysis and Forecasting (Part B) 595

Chapter 19. Parameter Estimation Beyond Curve-Fitting: MLE (With an Application to Binary-Choice Models) and GMM (With an Application to IV Regression) 647

Chapter 20. Concluding Comments 681

Mathematics Review 693

Index 699 

Richard Ashley is a professory of Economics at Virginia Tech. He earned his Ph.D in 1976 at the University of California, San Diego. Prior to VT, he taught economics at the University of Texas, Austin. His specialties and areas of interest include Econometrics and Macroeconomic Forecasting. He has received several teaching and research grants and has been published in Macroeconomic Dynamics, Journal of Applied Econometrics, Econometric Reviews, International Review of Economics and Finance, among others.