Advanced Texts in Econometrics
2 total works
Modelling Non-Linear Economic Relationships
by Clive W. J. Granger and Timo Terasvirta
Published 1 October 1993
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.
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.
Modelling Nonlinear Economic Time Series
by Timo Terasvirta, Dag Tjostheim, and Clive W. J. Granger
Published 16 December 2010
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 in practice. For this purpose, 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.
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.