Predictions in Time Series Using Regression Models

Frantisek Stulajter (Autor)

Buch | Hardcover

233 Seiten

2002 | 2002 ed.
Springer-Verlag New York Inc.
978-0-387-95350-2 (ISBN)

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Books on time series models deal mainly with models based on Box-Jenkins methodology which is generally represented by autoregressive integrated moving average models or some nonlinear extensions of these models, such as generalized autoregressive conditional heteroscedasticity models. Statistical inference for these models is well developed and commonly used in practical applications, due also to statistical packages containing time series analysis parts. The present book is based on regression models used for time series. These models are used not only for modeling mean values of observed time se ries, but also for modeling their covariance functions which are often given parametrically. Thus for a given finite length observation of a time series we can write the regression model in which the mean value vectors depend on regression parameters and the covariance matrices of the observation depend on variance-covariance parameters. Both these dependences can be linear or nonlinear. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models.

1 Hilbert Spaces and Statistics.- 2 Random Processes and Time Series.- 3 Estimation of Time Series Parameters.- 4 Predictions of Time Series.- 5 Empirical Predictors.- References.

Zusatzinfo	5 Illustrations, black and white; IX, 233 p. 5 illus.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
ISBN-10	0-387-95350-7 / 0387953507
ISBN-13	978-0-387-95350-2 / 9780387953502
Zustand	Neuware