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More About This Title Fundamentals of Applied Econometrics
English
English
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.
English
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
