Description
Book SynopsisFundamentals 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,
Table of Contents
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
