Druck auf Anfrage Neuware - Printed after ordering - This book provides an overview of the current state-of-the-art of nonlinear time series analysis,richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a'theorem-proof' format, it shows concrete applications on a variety of empirical time series. Thebook can be used in graduate courses in nonlinear time series and at the same time also includesinteresting material for more advanced readers. Though it is largely self-contained, readers requirean understanding of basic linear time series concepts, Markov chains and Monte Carlo simulationmethods.The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end ofevery chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

Bestandsnummer des Verk�ufers 9783319432519

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Bibliografische Details

Titel: Elements of Nonlinear Time Series Analysis and Forecasting
Verlag: Springer International Publishing, Springer Nature Switzerland
Erscheinungsjahr: 2017
Sprache: Englisch
ISBN-10: 3319432516
ISBN-13: 9783319432519
Einband: Buch
Zustand: Neu

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This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a "theorem-proof" format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods.

The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.

To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

�ber die Autorin bzw. den Autor

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 Ume� (Sweden), British Columbia (Canada) and Montpellier II (France), as well as Royal Holloway College (London, UK).

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