Econometrics

"An introductory PhD-level textbook for one of the first and most foundational courses every economics graduate student must take"--

"Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. 'Econometrics' provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds ; draws on integrated, research-level datasets, provided on an accompanying website ; discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning ; features hundreds of exercises that enable students to learn by doing ; includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples ; can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s 'Probability and Statistics for Economists'.--taken from back cover.

"This textbook is the second in a two-part series covering the core material typically taught in a one-year Ph.D. course in econometrics. The sequence is : 1. 'Probability and Statistics for Economists' (first volume) ; 2. 'Econometrics' (this volume). 'Econometrics' assumes that students have a background in multivariate calculus, probability theory, linear algebra, and mathematical statistics. A prior course in undergraduate econometrics would be helpful but is not required. The relevant background in probability theory and mathematical statistics is provided in 'Probability and Statistics for Economists'. For reference, the basic tools of matrix algebra and probability inequalites are reviewed in Appendixes A and B. This textbook contains more material than can be covered in a one-semester course. This is intended to provide instructors flexibility concerning which topics to cover, which to cover in depth, and which to cover briefly. Some material is suitable for second-year Ph.D. instruction."--adapted from Preface, page xxv.

Preface -- Acknowledgments -- Notation -- Chapter 1. Introduction -- Part I. Regression. Chapter 2. Conditional expectation and projection -- Chapter 3. The algebra of least squares -- Chapter 4. Least squares regression -- Chapter 5. Normal regression -- Part II. Large Sample Methods. Chapter 6. A review of large sample asymptotics -- Chapter 7. Asymptotic theory for least squares -- Chapter 8. Restricted estimation -- Chapter 9. Hypothesis testing -- Chapter 10. Resampling methods -- Part III. Multiple Equation Models. Chapter 11. Multivariate regression -- Chapter 12. Instrumental variables -- Chapter 13. Generalized method of moments -- Part IV. Dependent and Panel Data. Chapter 14. Time series -- Chapter 15. Multivariate time series -- Chapter 16. Nonstationary time series -- Chapter 17. Panel data -- Chapter 18. Difference in differences -- Part V. Nonparametric Methods. Chapter 19. Nonparametric regression -- Chapter 20. Series regression -- Chapter 21. Regression discontinuity -- Part VI. Nonlinear Methods. Chapter 22. M-Estimators -- Chapter 23. Nonlinear least squares -- Chapter 24. Quantile regression -- Chapter 25. Binary choice -- Chapter 26. Multiple choice -- Chapte…