Principles of Econometrics 5th Edition By R. Carter Hill, William E. Griffiths
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- Pages
- 907
- File size
- 12.56 MB
- Format
- Digital PDF
- Course
- Financial Economics
- Category
- eBook[PDF]
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About this ebook
Principles of Econometrics, 5th Edition by R. Carter Hill, William E. Griffiths, and Guay C. Lim is a premier introductory textbook designed for undergraduate economics and finance students, as well as first-year graduate students. Published by Wiley, this textbook avoids abstract matrix algebra and heavily math-intensive proof formats. Instead, it focuses on helping students develop practical, working skills to estimate, model, and forecast using real-world data. [1, 2, 3, 4]
Key Text Details
- Authors: R. Carter Hill, William E. Griffiths, Guay C. Lim
- Publisher: John Wiley & Sons
- Print ISBNs: 9781118452271, 1118452275
- Digital/eTextbook ISBNs: 9781119320944, 1119320941
- Official Data & Resources: Principles of Econometrics Website (hosts data files for Stata, EViews, R, SAS, and Excel) [1, 2, 3]
Main Features of the 5th Edition
- Core Focus: Concentrates on motivation, intuition, and real economic applications rather than a strict theorem-proof format.
- No Matrix Algebra: Keeps math accessible; any necessary calculus concepts are clearly isolated in text appendices.
- Real-World Relevance: Features 25 to 30 new data-driven exercises at the end of each chapter.
- Data Observational Pivot: Re-engineered to explicitly acknowledge the observational nature of economic data right from the start. [1, 2, 3]
Core Chapter Breakdown
- An Introduction to Econometrics (Data collection and empirical paper writing)
- The Simple Linear Regression Model (Chapters 2–4: Foundations, interval estimation, hypothesis testing, and forecasting)
- The Multiple Regression Model (Chapters 5–7: Estimation, advanced inference, and indicator variables)
- Cross-Sectional Issues (Chapter 8: Heteroskedasticity and robust standard errors)
- Time-Series Data (Chapter 9: Regression with stationary variables and dynamic models)
- Endogenous Regressors (Chapters 10–11: Instrumental variables and simultaneous equation modeling) [1]
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