This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series. The statistical techniques explored analyse relationships between different variables, over time, such as the relationship between variables in a macroeconomy. Examples from Professor Teräsvirta's empirical work are given. Professors Granger and Teräsvirta are leading exponents of techniques of dynamic, multivariate analysis. They illustrate in this volume exploratory ways of using such techniques to provide models of nonlinear relationships between variables. This is an extension of previous work on linear relationships, and on univariate models. These developments will be of use to
econometricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships, such as an investment function, or a production function. Particular attention is paid to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. The book concentrates on stochastic series, since the existence of unexpected shocks strongly suggests that economic variables are stochastic. Granger and Teräsvirta also discuss the division of these nonlinear relationships into parametric and nonparametric models.
Readership: Graduate students in econometrics; academic economists/econometricians. Business economists
using econometric models.
Clive W. J. Granger, Professor, Centre for Econometric Analysis, California, and Timo Teräsvirta, Professor, Research Institute of the Finnish Economy, Finland
"`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