Time Series and Dynamic Models - Gourieroux, Christian

Time Series and Dynamic Models

Christian Gourieroux

Yayınevi: Cambridge University Press

Yayın tarihi: 12/1999

ISBN: 9780521423083

Yazar : Alain Monfort

İngilizce | 688 Sayfa | 152 mm x 229 mm

Tür: Ekonomi

  • Temin Süresi 28 - 42 iş günü


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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:
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

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