Modelling Non-Linear Time Series ATE

ISBN-13: 9780199587148 / Angielski / Twarda / 2011 / 432 str.

Timo Terasvirta; Dag Tjostheim; Clive W. J. Granger

Opis

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. Readership: Academics, researchers, graduates and advanced undergraduates of econometrics, particularly academics in time series econometrics.

O autorze

Timo Teräsvirta received his DPolSc (Econometrics) from the University of Helsinki in 1970. He has been Senior Research Fellow of the Academy of Finland (1972-76), Professor of Statistics at the University of Helsinki (1976-80), Visiting Scholar at CORE, Louvain-la-Neuve, (1978-79), Research Fellow at the Research Institute of the Finnish Economy (1980-89), Research Fellow at the Norges Bank, (1992-93, 1994, 2000), and Professor of Econometrics at the Stockholm School of Economics, (1994-2006). He has been Visiting Professor to several universities, including the University of California, San Diego, the University of Technology, Sydney, the Central European University, Budapest, and the Hanken School of Economics, Helsinki. Teräsvirta is an elected member of the International Statistical Institute, the Finnish Society of Sciences and Letters, Helsinki, and the Royal Academy of Sciences, Stockholm. Distinguished Author of Journal of Applied Econometrics and Fellow of Journal of Econometrics. Dag Tjøstheim holds a PhD in Applied Mathematics from Princeton University, 1974. He was Research Scientist at the seismic observatory NORSAR (1974-77) and Associate Professor at the Norwegian Business School (1977-80). He was Visiting Professor at the University of North Carolina, Chapel Hill (1983-84) and at the University of California, San Diego (1990-91). He has been working on time series and related areas in spatial processes including econometrics, fishery statistics, seismology and meteorology. Tjøstheim has served as main editor of the Scandinavian Journal of Statistics, and as Associate Editor of Bernoulli, Journal of the Royal Statistical Society Series B, and Journal of Time Series Analysis. He is the recipient of the Tjalling Koopmans Prize in Econometric Theory 1999-2002 and the Norwegian Sverdrup Prize 2009. He is elected member of the International Statistical Statistical Institute and the Norwegian Academy of Science. Clive W. J. Granger was Professor Emeritus at the University of California, San Diego. In 2003, he was awarded the Nobel Memorial Prize in Economic Sciences for fundamental discoveries in the analysis of time series data.