Predictions in Time Series Using Regression Models

Frantisek Stulajter

ISBN 10: 0387953507 / ISBN 13: 9780387953502

Published by Springer New York, 2002

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &nbsp-Appeals to finance analysts and econometricians&nbspThis book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people wh.

Seller Inventory # 458432383

Bibliographic Details

• Title: Predictions in Time Series Using Regression Models
• Publisher: Springer New York
• Publication Date: 2002
• Language: English
• ISBN 10: 0387953507
• ISBN 13: 9780387953502
• Binding: Gebunden
• Condition: New

About this title

Synopsis

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.

Review

From the reviews:

"The book is useful for students and researchers of mathematical statistics, economical and financial mathematics and management with an important mathematical focus." -Mathematical Reviews

"[This book] is an excellent reference book for researchers with an interest in mathematical statistics, econometric theory, or applied econometrics ... I strongly recommend it to anyone interested in learning a methodology for modeling and prediction of time series different from the Box-Jenkins approach. It would be an especially valuable resource for graduate students being introduced to the analysis of time series for self-study." –Journal of the American Statistical Association

"The book is well organized and carefully written with clear notation. It also stimulates the reader’s interest in wanting to learn more about some of the more advanced topics. ... Predictions in Time Series Using Regression Models is an excellent reference book for researchers ... . This book is a valuable addition to the extensive literature on the analysis of time series. I strongly recommend it ... . It would be an especially valuable resource for graduate students ... ." (Montserrat Fuentes, Journal of the American Statistical Association, Vol. 98 (463), September, 2003)

"This book is based on regression models used for time series. ... The aim of the book is to give a unified approach to the solution of statistical problems for such time series models, and mainly to problems of estimation of unknown parameters of models ... . The book is useful for students and researchers of mathematical statistics, economical and financial mathematics and management with an important mathematical focus." (Juan Carlos Abril, Mathematical Reviews, Issue 2003 h)

"The purpose of this book is to provide a unified approach for the estimation of regression parameters when the errors are correlated and stationary. ... The author uses the models to illustrate the methods described. ... the book should be useful for a person who is working in the areas of estimation and prediction." (T. Subba Rao, Short Book Reviews, Vol. 23 (1), 2003)