Elements of Nonlinear Time Series Analysis and Forecasting

Jan G. De Gooijer is Emeritus Professor of Economic Statistics at the University of Amsterdam. He completed an M.Sc. degree in mathematical statistics at Delft Technical University and a Ph.D. in economics at the Vrije Universiteit ("Free University") Amsterdam. He has (co-)authored over 100 publications on forecasting, time series analysis, econometrics, and statistics. Jan has been Associate Editor, Editor and Editor-in-Chief of The International Journal of Forecasting, Associate Editor of the Journal of Forecasting, and he has served on the editorial board of Empirical Economics. He is an elected member of the International Statistical Institute, and an Honorary Fellow of the International Institute of Forecasters. He has held visiting professor positions at the Universities of Umeå (Sweden), British Columbia (Canada) and Montpellier II (France), as well as Royal Holloway College (London, UK).

Introduction and Some Basic Concepts.- Classic Nonlinear Models.- Probabilistic Properties.- Frequency-Domain Tests.- Time-Domain Linearity Tests.- Model Estimation, Selection and Checking.- Tests for Serial Independence.- Time-Reversibility.- Semi- and Nonparametric Forecasting.- Forecasting Vector Parametric Models and Methods.- Vector Semi- and Nonparametric Methods. 

 "The book describes main statistical procedures used in modern nonlinear time series analysis. ... Each chapter ends with a section containing various exercises, both theoretical and simulation, which makes the book suitable for a graduate course in nonlinear time series. Each chapter also contains a section with useful information about the existing software (mainly in MATLAB and R) related to the topic of the chapter." (Vytautas Kazakevicius, Mathematical Reviews, January, 2018)
"This is an excellent addition to the library of books on time series analysis. The most attractive feature of this book is that it places importance on developing intuition about nonlinear time series rather than the more formal theorem-proof approach. It is abundant with data examples and simulations that enhance understanding of the stochastic properties of the models. In my opinion, the approach taken is the best pedagogical technique to learn about time series models." (Hernando Ombao, Journal of the American Statistical Association JASA, Vol. 113 (522), 2018)

"For the scientific quality of its content I do not exaggerate if I consider this book as a treasure." (Oscar Busto, zbMATH 1376.62001, 2018)