"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, studentswill learn by doing. Ashley provides students with integrated, hands-on exercises, and the text is supplemented with Active Learning Exercises"--
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.
Whats 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
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.
