Synopses & Reviews
Modern Techniques for Modern Time-Series Analysis!
Assuming only a basic understanding of multiple regression analysis, the accessible introduction to time-series analysis shows how to develop models capable of forecasting, interpreting, and testing hypotheses concerning economic data using modern techniques.
This new edition reflects recent advances in time-series econometrics, such as out-of-sample forecasting techniques, nonlinear time-series models, Monte Carlo analysis, and bootstrapping. Numerous examples from fields ranging from agricultural economics to transnational terrorism illustrate the techniques.
- Detailed example using real-world data illustrate key concepts.
- Present a straightforward, step-by-step approach to time-series estimation.
- A large number of questions and empirical exercises enable you to practice the techniques covered in the text.
- Data sets are available on the text’s Web site.
- Emphasizes difference equations as the foundation of all time-series models.
asked three review editors to rate their top five favorite books in the September 2003 issue. The first edition of Applied Econometric Time Series
was among those chosen.
This new edition reflects recent advances in time-series econometrics, such as out-of-sample forecasting techniques, non-linear time-series models, Monte Carlo analysis, and bootstrapping. Numerous examples from fields ranging from agricultural economics to transnational terrorism illustrate various techniques.
About the Author
Walter Enders, is the Lee Bidgood Chair of Economics at the University of Alabama. He received his doctorate in economics from Columbia University in New York. His research focuses on time-series econometrics with a special emphasis on the dynamic aspects of terrorism. He has published over fifty articles including those in the American Economic Review, the American Political Science Review, and the Journal of Business and Economics Statistics.
Table of Contents
About the Authors.
Chapter 1. Difference Equations.
Chapter 2. Stationary Time-Series Models.
Chapter 3. Modeling Volatility.
Chapter 4. Models with Trend..
Chapter 5. Multiequation Time-Series Models.
Chapter 6. Cointegration and Error-Correction Models.
Chapter 7. Nonlinear Time-Series Models.