Buch, Englisch, 437 Seiten, Paperback, Format (B × H): 210 mm x 279 mm, Gewicht: 1093 g
Reihe: Springer Texts in Statistics
Buch, Englisch, 437 Seiten, Paperback, Format (B × H): 210 mm x 279 mm, Gewicht: 1093 g
Reihe: Springer Texts in Statistics
ISBN: 978-1-4757-7750-5
Verlag: Springer
The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models.
The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
Weitere Infos & Material
Stationary Processes.- ARMA Models.- Spectral Analysis.- Modeling and Forecasting with ARMA Processes.- Nonstationary and Seasonal Time Series Models.- Multivariate Time Series.- State-Space Models.- Forecasting Techniques.- Further Topics.- Erratum.