Time series and dynamic models

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

Available:*

Bound With These Titles

On Order

Summary

Summary

Table of Contents