Modelling nonlinear economic time series / Timo Terasvirta, Dag Tjostheim and Clive W.J. Granger.

Includes bibliographical references and indexes.

1. Concepts, models and definitions ;
2. Nonlinear models in economic theory ;
3. Parametric nonlinear models ;
4. The nonparametric approach ;
5. Testing linearity against parametric alternatives ;
6. Testing parameter constancy ;
7. Nonparametric specification tests ;
8. Models of conditional heteroskedasticity ;
9. Time-varying parameters and state space models ;
10. Nonparametric models ;
11. Nonlinear and nonstationary models ;
12. Algorithms for estimating parametric nonlinear models ;
13. Basic nonparametric estimates ;
14. Forecasting from nonlinear models ;
15. Nonlinear impulse responses ;
16. Building nonlinear models ;
17. Other topics

This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models to practice. The building of various nonlinear models with its three stages of model building- specification, estimation, and evaluation- is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried out using numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones. Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter is devoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.