This volume in the series Advanced Texts in Econometrics explains recent theoretical developments in the econometric modelling of relationships between different statistical series. Clive Granger and Timo TerAsvirta illustrate ways of using dynamic, multivariate analysis techniques to provide models of nonlinear relationships between variables. They pay particular attention to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. They also discuss the division of nonlinear relationships into parametric and nonparametric models. The developments detailed in this book will be useful to econometricians who need to construct or use models of nonlinear, dynamic, multivariate relationships, such as an investment or production function.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
`Very good overview of tests of linearity.'
D. Malliaropulos, London Guildhall University
`It is an excellent survey in a very important area.'
Dr D. Basu, University of Kent
'provides the reader with a clear and thoughtfully structured guide to the fundamentals of nonlinear time series modelling and highlights the considerable potential of these modelling techniques for applied econometricians ... The authors should be congratulated on bringing this important class of models more fully into the mainstream realm.'
S.J. Leybourne, The Economic Journal, Vol 105, No. 428, January 1995
`For a reader with a basic knowledge of time series analysis concepts, this book offers an excellent opportunity to catch up on the explosive developments in modeling nonlinear times series ... It is very readable ... An intermediate-to-advanced level of knowledge can be gained by reading the book in its entirety. This would be an ideal resource for a special topics graduate level seminar. For those with great interest in specific topics, there is an
extensive reference section ... it is a wise addition to the practicing time-series analyst's reference shelf, whether to dabble or to delve extensively into modeling nonlinear time series.'
Journal of American Statistical Association
Basic concepts; general models and tools for analysis; nonlinear models in economic theory; particular nonlinear multivariate models; long memory models; linearity testing; building nonlinear models; forecasting, aggression and non-symmetry; applications; strategies for nonlinear modelling.