Time Series and Dynamic Models (Themes in Modern Econometrics)

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Time Series and Dynamic Models (Themes in Modern Econometrics)

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  • 製本 Hardcover:ハードカバー版/ページ数 668 p.
  • 言語 ENG
  • 商品コード 9780521411462
  • DDC分類 330.0151955


Edited and translated by Giampiero Gallo. This book provides a unified and comprehensive analysis of the full range of topics that comprise modern time series econometrics.

Full Description

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.

Table of Contents

Part I. Traditional Methods: 1. Introduction
2. Seasonal adjustment by regression methods
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 approach to 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. Non-stationary processes
Part IV. State Space Models: 15. State space
models and the Kalman filter
16. Applications of the state space model
Statistical tables