296 pages | 50 B/W Illus.
Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics:
The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.
'This book provides an intuitive and easy-to-access introduction to econometrics. A variety of topics of econometrics suitable for undergraduate students are discussed in the book along with stimulating examples and computer codes. This book certainly deserves a high recommendation for undergraduate students.' — Professor In Choi, Department of Economics, Sogang University, Korea
'The book provides a clear introduction to applied econometrics and is recommended for someone who wants a grounding in basic econometrics. Prof Min is to be congratulated on the simple but effective way in which he presents his ideas.' — Ferdinand A. Gul, Alfred Deakin Professor, Department of Accounting, Faculty of Business and Law, Deakin University, Australia
1. Review of Estimation and Hypothesis Tests
2. Simple Linear Regression Models
3. Multiple Linear Regression Models
4. Dummy Explanatory Variables
5. More on Multiple Regression Analysis
6. Endogeneity and Two-Stage Least Squares Estimation
7. Models for Panel Data
8. Simultaneous Equations Models
9. Vector Autoregressive (VAR) Models
10. Autocorrelation and ARCH/GARCH
11. Unit Root, Cointegration and Error Correction Model
12. Qualitative and Limited Dependent Variable Models
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