This book explores recent theoretical and practical developments in the econometric modelling of relationships between economic time series. The techniques discussed are concerned with the nonlinear relationship between stochastic variables, such as those encountered in parts of macroeconomics, such as investment or a production functions. Examples of empirical work are given, including some produced by Professor Terasvirta. Professors Granger and Terasvirta 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 economatricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships. 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. Questions of estimation, testing and evaluation of such models are considered carefully. The types of models discussed include parametric and non-parametric, for example neural networks and projection pursuit, and particular attention is paid to smooth regime-switching models. --back cover