Predictions in Time Series Using Regression Models

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 toillustrate 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)