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 texts 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. Starting with a unique Statistics review to start the book, students will learn by doing. Ashley provides students with integrated, hands-on exercises, and the text is supplemented with Active Learning Exercises.
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.
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 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