This monograph discusses the decomposition of a vector of time series, described by a linear state-space model, into trend, cyclical, seasonal, irregular and input-related components. The representation and signal-extraction methods employed provide unique advantages, notably the ability to decompose single or multiple time series, the lack of revisions as the sample increases or the independence of the method from specific model formulations. Besides a complete and self-contained presentation of this subject, this text emphasizes in the practical application of the methods described. To this end, each chapter includes several examples of the methods proposed and Appendix A provides a broad description of E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from www.ucm.es/info/icae/e4. An Appendix provides the source code and data references required to replicate all the practical examples.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
This monograph discusses the decomposition of a vector of time series, described by a linear state-space model, into trend, cyclical, seasonal, irregular and input-related components. The representation and signal-extraction methods employed provide unique advantages, notably the ability to decompose single or multiple time series, the lack of revisions as the sample increases or the independence of the method from specific model formulations. Besides a complete and self-contained presentation of this subject, this text emphasizes in the practical application of the methods described. To this end, each chapter includes several examples of the methods proposed and Appendix A provides a broad description of E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from www.ucm.es/info/icae/e4. An Appendix provides the source code and data references required to replicate all the practical examples.
Teaches Econometrics at Universidad Complutense de Madrid. He is engaged with his colleagues Jose Casals and Sonia Sotoca in a long-term project to apply state-space methods to standard econometric problems. An output of this project is E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from www.ucm.es/info/icae/e4
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph discusses the decomposition of a vector of time series, described by a linear state-space model, into trend, cyclical, seasonal, irregular and input-related components. The representation and signal-extraction methods employed provide unique advantages, notably the ability to decompose single or multiple time series, the lack of revisions as the sample increases or the independence of the method from specific model formulations. Besides a complete and self-contained presentation of this subject, this text emphasizes in the practical application of the methods described. To this end, each chapter includes several examples of the methods proposed and Appendix A provides a broad description of E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from ucm.es/info/icae/e4. An Appendix provides the source code and data references required to replicate all the practical examples. 112 pp. Englisch. N° de réf. du vendeur 9783845402840
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jerez MiguelTeaches Econometrics at Universidad Complutense de Madrid. He is engaged with his colleagues Jose Casals and Sonia Sotoca in a long-term project to apply state-space methods to standard econometric problems. An output of . N° de réf. du vendeur 5480442
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Taschenbuch. Etat : Neu. Neuware -This monograph discusses the decomposition of a vector of time series, described by a linear state-space model, into trend, cyclical, seasonal, irregular and input-related components. The representation and signal-extraction methods employed provide unique advantages, notably the ability to decompose single or multiple time series, the lack of revisions as the sample increases or the independence of the method from specific model formulations. Besides a complete and self-contained presentation of this subject, this text emphasizes in the practical application of the methods described. To this end, each chapter includes several examples of the methods proposed and Appendix A provides a broad description of E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from ucm.es/info/icae/e4. An Appendix provides the source code and data references required to replicate all the practical examples.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch. N° de réf. du vendeur 9783845402840
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph discusses the decomposition of a vector of time series, described by a linear state-space model, into trend, cyclical, seasonal, irregular and input-related components. The representation and signal-extraction methods employed provide unique advantages, notably the ability to decompose single or multiple time series, the lack of revisions as the sample increases or the independence of the method from specific model formulations. Besides a complete and self-contained presentation of this subject, this text emphasizes in the practical application of the methods described. To this end, each chapter includes several examples of the methods proposed and Appendix A provides a broad description of E4, a free MATLAB Toolbox for time series analysis that can be freely downloaded from ucm.es/info/icae/e4. An Appendix provides the source code and data references required to replicate all the practical examples. N° de réf. du vendeur 9783845402840
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock. N° de réf. du vendeur 3845402849
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