Time Series and Dynamic Models

An up-to-date and comprehensive analysis of traditional and modern time series econometrics.

Christian Gourieroux (Author), Alain Monfort (Author), Giampiero Gallo (Translated by)

9780521423083, Cambridge University Press

Paperback, published 13 January 1997

688 pages, 112 b/w illus. 50 tables
22.9 x 15.2 x 3.8 cm, 1 kg

'This is a well done introduction to both classical and modern time series models and techniques. Throughout, the authors have managed to keep a sound balance between mathematical rigor (which is always present, but never emphasized or celebrated for its own sake) and user-friendliness of presentation. I found this mixture very easy to digest. Another strong point of the book is its technical competence. In almost every line, one feels that two of the most brilliant present-day theoretical econometricians are at work. Review in Statistical Papers

In this book Christian Gourieroux and Alain Monfort provide an up-to-date and comprehensive analysis of modern time series econometrics. They have succeeded in synthesising in an organised and integrated way a broad and diverse literature. While the book does not assume a deep knowledge of economics, one of its most attractive features is the close attention it pays to economic models and phenomena throughout. The coverage represents a major reference tool for graduate students, researchers and applied economists. The book is divided into four sections. Section one gives a detailed treatment of classical seasonal adjustment or smoothing methods. Section two provides a thorough coverage of various mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to time series econometric problems.

Preface
1. Introduction
Part I. Traditional Methods: 2. Linear regression for seasonal adjustment
3. Moving averages for seasonal adjustment
4. Exponential smoothing methods
Part II. Probabilistic and Statistical Properties of Stationary Processes: 5. Some results on the univariate processes
6. The Box and Jenkins method for forecasting
7. Multivariate time series
8. Time-series representations
9. Estimation and testing (stationary case)
Part III. Time-series Econometrics: Stationary and Nonstationary Models: 10. Causality, exogeneity, and shocks
11. Trend components
12. Expectations
13. Specification analysis
14. Statistical properties of nonstationary processes
Part IV. State-space Models: 15. State-space models and the Kalman filter
16. Applications of the state-space model
References
Tables
Index.

Subject Areas: Econometrics [KCH]