Synopses & Reviews
Applied Econometric Time Series by Walter Enders of Iowa State University This accessible and comprehensive review of the most recent advances in time series analysis requires some background in multiple regression analysis. Text examples provide a balance between macro and microeconomic applications and are drawn from varied sources including agricultural economics, international finance, and transnational terrorism. This ground-breaking new title is part of the prestigious "Wiley Series in Probability and Statistics." Intended for Masters/Ph.D. courses in Time Series Analysis, Methods or Econometrics, Econometrics II or III, or Advanced Macroeconomics found in departments of Economics, Statistics, Mathematics, Management Science or Industrial Engineering. Features:
* Difference equations are used as the building blocks of all time series models (Chs. 1 & 2).
* Emphasizes non-stationary time series to aid applied research (Chs. 3, 4, & 6).
* Many techniques are illustrated with detailed examples from current international finance literature. For example, purchasing power parity illustrates unit root tests and cointegration.
* Includes intriguing examples concerning econometric models of transnational terrorism.
* Detailed examples of each procedure are provided including a step-by-step summary of each of the procedure's stages.
* Includes problems for each chapter and data is supplied on disk for all end of chapter exercises.
Synopsis
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.
Features:
- 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.
Synopsis
Amstat Newsasked three review editors to rate their top five favorite books in the September 2003 issue. The first edition of
Applied Econometric Time Serieswas 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.
Synopsis
Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue.
Applied Econometric Times Series was among those chosen.
Unique in that it covers modern time series analysis from the sole prerequisite of an introductory course in multiple regression analysis. Describes the theory of difference equations, demonstrating that they are the foundation of all time-series models with emphasis on the Box-Jenkins methodology. Considers many recent developments in time series analysis including unit root tests, ARCH models, cointegration/error-correction models, vector autoregressions and more. There are numerous examples to illustrate various techniques, many of which concern econometric models of transnational terrorism. The accompanying disk provides data for students to work with.
Description
Includes bibliographical references (p. 423-426) and indexes.
Table of Contents
Preface.
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.
Statistical Tables.
References.
Index.