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.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

This book, and its companion volume, present a collection of papers by Clive W.J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

This book, and its companion volume, present a collection of papers by Clive W.J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Presenting an economic perspective of deforestation in the Brazilan Amazon, this study utilizes economic and ecological data from 1970 to 1996. It examines the extent to which land clearing promotes economic activity and growth and analyzes policies such as road building and subsidized credit. It explores whether the economic benefits of land clearing surpass the ecological costs and considers the viability of extractivism as an alternative to deforestation.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

In these three essays, Professor Granger explains the process of constructing and evaluating an empirical model. Drawing on a wide range of cases and vignettes from economics, finance, politics and environment economics, as well as from art, literature, and the entertainment industry, Professor Granger combines rigor with intuition to provide a unique and entertaining insight into one of the most important subjects in modern economics. Chapter 1 deals with Specification. Chapter 2 considers Evaluation, and argues that insufficent evaluation is undertaken by economists, and that models should be evaluated in terms of the quality of their output. In Chapter 3, the question of how to evaluate forecasts is considered at several levels of increasing depth.

In these three essays, Professor Granger explains the process of constructing and evaluating an empirical model. Drawing on a wide range of cases and vignettes from economics, finance, politics and environment economics, as well as from art, literature, and the entertainment industry, Professor Granger combines rigour with intuition to provide a unique and entertaining insight into one of the most important subjects in modern economics. Chapter 1 deals with Specification. The process of specifying a model is discussed using deforestation in the Amazon region of Brazil as an illustration. Chapter 2 considers Evaluation, and argues that insufficent evaluation is undertaken by economists, and that models should be evaluated in terms of the quality of their output. In Chapter 3, the question of how to evaluate forecasts is considered at several levels of increasing depth and using a more sophisticated, technical approach than in the earlier two chapters.

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.

The series Advanced Texts in Econometrics allows leading econometricians to summarize the theretical areas in which they have made a contribution. This volume surveys and summarizes new work linking theoretical developments in nonlinear analysis to current models of the economy.